rencontre gay sur vence 1,476 reviews of ICLR 2019 conference were released in last early November. Reviewers’ comments are also available from the Open Review website for ICLR2019. However, there was no a feature for sorting by review score, and there were so many papers that I didn’t know what to read first, so I make a sorted list with review scores.

buy Lyrica online usa There are two scales of review score: Rating and Confidence. The Rating is the score the reviewer gives to each paper. In other words, whether to accept or reject a paper and it represent the quality of the paper. The Confidence indicates how reviewers understand the area they are reviewing. If the reviewer is confident in his/her opinion, he/her give it as a high score. The higher the numbers for both is better in most cases. However, I just calculated it as a rough job on averaging the Ratings and sorted it in decreasing order.

### The meaning of Rating

- 1: Trivial or wrong
- 2: Strong rejection
- 3: Clear rejection
- 4: Ok but not good enough – rejection
- 5: Marginally below acceptance threshold
- 6: Marginally above acceptance threshold
- 7: Good paper, accept
- 8: Top 50% of accepted papers, clear accept
- 9: Top 15% of accepted papers, strong accept
- 10: Top 5% of accepted papers, seminal paper

### The meaning of Confidence

- 1: The reviewer’s evaluation is an educated guess
- 2: The reviewer is willing to defend the evaluation, but it is quite likely that the reviewer did not understand central parts of the paper
- 3: The reviewer is fairly confident that the evaluation is correct
- 4: The reviewer is confident but not absolutely certain that the evaluation is correct
- 5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

### Note

try this out 2 to 5 reviewers per paper, but usually 3 to 4.

site de rencontre gratuit pour ado gay I sorted the papers in descending order with a Rating average. In the list below, the meaning of each value is rank, an average of all Ratings, a standard deviation of Ratings, all Rating scores/ all Confidence scores and title.

In addition, after I made the list below and

However, some scores are correct on the bottom list because it’s more recent updates than the site. So I’m going to look at what I made.

If that’s helpful, look at the list below and let me know if you find any interesting papers.

- 8.67, 1.25, [9, 10, 7] [3, 5, 3]GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING
- 8.33, 0.94, [9, 9, 7] [4, 5, 3]Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
- 8.33, 1.25, [10, 7, 8] [4, 3, 4]Large Scale GAN Training for High Fidelity Natural Image Synthesis
- 8.33, 1.70, [9, 6, 10] [5, 4, 5]ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA
- 8.00, 0.82, [7, 9, 8] [5, 5, 3]Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
- 8.00, 0.82, [8, 7, 9] [4, 3, 4]Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
- 8.00, 0.82, [7, 9, 8] [4, 5, 4]Slimmable Neural Networks
- 8.00, 0.00, [8, 8, 8] [4, 2, 5]Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
- 8.00, 0.82, [7, 9, 8] [5, 4, 4]Temporal Difference Variational Auto-Encoder
- 8.00, 1.63, [8, 10, 6] [3, 4, 3]Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
- 8.00, 0.00, [8, 8, 8] [3, 4, 3]Unsupervised Learning of the Set of Local Maxima
- 8.00, 0.82, [7, 8, 9] [4, 4, 5]An Empirical Study of Example Forgetting during Deep Neural Network Learning
- 8.00, 0.82, [7, 9, 8] [4, 4, 5]Posterior Attention Models for Sequence to Sequence Learning
- 7.67, 0.47, [7, 8, 8] [3, 4, 3]Smoothing the Geometry of Probabilistic Box Embeddings
- 7.67, 1.25, [8, 6, 9] [4, 4, 4]ON RANDOM DEEP AUTOENCODERS: EXACT ASYMPTOTIC ANALYSIS, PHASE TRANSITIONS, AND IMPLICATIONS TO TRAINING
- 7.67, 0.94, [9, 7, 7] [4, 2, 3]Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
- 7.67, 1.70, [6, 10, 7] [4, 3, 3]Identifying and Controlling Important Neurons in Neural Machine Translation
- 7.67, 1.25, [6, 8, 9] [5, 4, 4]Critical Learning Periods in Deep Networks
- 7.67, 1.25, [8, 9, 6] [4, 4, 4]Sparse Dictionary Learning by Dynamical Neural Networks
- 7.67, 1.70, [7, 10, 6] [4, 4, 4]KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks
- 7.67, 0.47, [8, 7, 8] [3, 3, 4]Learning Unsupervised Learning Rules
- 7.67, 0.94, [9, 7, 7] [3, 4, 4]Learning Robust Representations by Projecting Superficial Statistics Out
- 7.67, 0.94, [9, 7, 7] [5, 5, 4]A2BCD: Asynchronous Acceleration with Optimal Complexity
- 7.67, 0.47, [8, 7, 8] [4, 4, 4]Pay Less Attention with Lightweight and Dynamic Convolutions
- 7.67, 1.25, [8, 9, 6] [4, 4, 4]Supervised Community Detection with Line Graph Neural Networks
- 7.67, 0.47, [8, 7, 8] [2, 4, 3]Robustness May Be at Odds with Accuracy
- 7.67, 0.47, [7, 8, 8] [4, 4, 3]Kernel Change-point Detection with Auxiliary Deep Generative Models
- 7.67, 0.47, [8, 8, 7] [4, 4, 4]Adaptive Input Representations for Neural Language Modeling
- 7.67, 0.47, [7, 8, 8] [4, 3, 3]A Variational Inequality Perspective on Generative Adversarial Networks
- 7.67, 0.94, [7, 9, 7] [4, 4, 4]Composing Complex Skills by Learning Transition Policies with Proximity Reward Induction
- 7.67, 0.47, [7, 8, 8] [4, 3, 4]Towards Robust, Locally Linear Deep Networks
- 7.50, 0.50, [7, 8, 8, 7] [4, 3, 3, 4]On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
- 7.50, 2.29, [7, 10, 9, 4] [4, 4, 5, 4]Exploration by random network distillation
- 7.33, 1.70, [9, 8, 5] [4, 3, 4]Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer
- 7.33, 1.25, [7, 6, 9] [3, 4, 3]Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
- 7.33, 0.47, [7, 7, 8] [4, 2, 3]Dynamic Sparse Graph for Efficient Deep Learning
- 7.33, 0.47, [8, 7, 7] [5, 4, 3]Differentiable Learning-to-Normalize via Switchable Normalization
- 7.33, 0.47, [7, 8, 7] [4, 4, 3]Learning to Remember More with Less Memorization
- 7.33, 1.25, [7, 9, 6] [3, 5, 4]Large-Scale Study of Curiosity-Driven Learning
- 7.33, 0.47, [7, 8, 7] [1, 5, 5]Evaluating Robustness of Neural Networks with Mixed Integer Programming
- 7.33, 0.47, [8, 7, 7] [4, 3, 3]Small nonlinearities in activation functions create bad local minima in neural networks
- 7.33, 0.47, [7, 7, 8] [3, 3, 2]Approximability of Discriminators Implies Diversity in GANs
- 7.33, 0.47, [7, 7, 8] [4, 3, 4]Diversity is All You Need: Learning Skills without a Reward Function
- 7.33, 0.47, [8, 7, 7] [4, 4, 5]Deep Frank-Wolfe For Neural Network Optimization
- 7.33, 1.25, [9, 7, 6] [3, 3, 3]ProMP: Proximal Meta-Policy Search
- 7.33, 0.47, [7, 8, 7] [2, 2, 4]Efficient Training on Very Large Corpora via Gramian Estimation
- 7.33, 1.25, [6, 9, 7] [5, 4, 4]Gradient descent aligns the layers of deep linear networks
- 7.33, 0.94, [6, 8, 8] [3, 4, 4]Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
- 7.33, 0.47, [7, 8, 7] [5, 4, 3]Time-Agnostic Prediction: Predicting Predictable Video Frames
- 7.33, 2.36, [4, 9, 9] [4, 4, 5]Biologically-Plausible Learning Algorithms Can Scale to Large Datasets
- 7.33, 0.47, [7, 8, 7] [5, 4, 4]Towards Metamerism via Foveated Style Transfer
- 7.33, 0.47, [7, 7, 8] [5, 5, 5]Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control
- 7.33, 0.47, [8, 7, 7] [4, 5, 3]LanczosNet: Multi-Scale Deep Graph Convolutional Networks
- 7.33, 0.47, [8, 7, 7] [4, 4, 3]Visualizing and Understanding Generative Adversarial Networks
- 7.33, 1.25, [9, 6, 7] [4, 4, 4]Label super-resolution networks
- 7.00, 0.82, [7, 8, 6] [4, 4, 4]Deep, Skinny Neural Networks are not Universal Approximators
- 7.00, 0.82, [8, 7, 6] [3, 5, 2]DARTS: Differentiable Architecture Search
- 7.00, 1.41, [6, 9, 6] [3, 5, 4]Diffusion Scattering Transforms on Graphs
- 7.00, 1.63, [7, 5, 9] [5, 3, 4]ADVERSARIAL DOMAIN ADAPTATION FOR STABLE BRAIN-MACHINE INTERFACES
- 7.00, 0.00, [7, 7, 7] [4, 2, 2]CoT: Cooperative Training for Generative Modeling of Discrete Data
- 7.00, 0.82, [6, 7, 8] [3, 4, 4]An analytic theory of generalization dynamics and transfer learning in deep linear networks
- 7.00, 0.00, [7, 7, 7] [5, 3, 3]Deterministic Variational Inference for Robust Bayesian Neural Networks
- 7.00, 0.82, [6, 7, 8] [3, 4, 4]Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
- 7.00, 0.00, [7, 7, 7] [3, 4, 4]EMI: Exploration with Mutual Information Maximizing State and Action Embeddings
- 7.00, 0.00, [7, 7, 7] [3, 4, 3]Learning a SAT Solver from Single-Bit Supervision
- 7.00, 0.00, [7, 7, 7] [5, 4, 4]Generative Code Modeling with Graphs
- 7.00, 0.82, [8, 6, 7] [4, 2, 4]Meta-Learning Probabilistic Inference for Prediction
- 7.00, 0.00, [7, 7, 7] [4, 3, 4]Relaxed Quantization for Discretized Neural Networks
- 7.00, 0.82, [7, 8, 6] [4, 2, 4]Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
- 7.00, 0.00, [7, 7, 7] [4, 4, 5]Auxiliary Variational MCMC
- 7.00, 1.41, [9, 6, 6] [4, 4, 5]SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY
- 7.00, 1.63, [7, 5, 9] [3, 4, 4]Deep Graph Infomax
- 7.00, 0.00, [7, 7, 7] [4, 5, 3]Riemannian Adaptive Optimization Methods
- 7.00, 0.82, [8, 7, 6] [2, 3, 4]Detecting Egregious Responses in Neural Sequence-to-sequence Models
- 7.00, 0.82, [7, 8, 6] [5, 5, 3]Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution
- 7.00, 0.00, [7, 7, 7] [5, 2, 4]How Important is a Neuron
- 7.00, 0.00, [7, 7, 7] [3, 3, 3]Neural network gradient-based learning of black-box function interfaces
- 7.00, 0.82, [8, 6, 7] [4, 5, 4]Wizard of Wikipedia: Knowledge-Powered Conversational Agents
- 7.00, 2.16, [9, 4, 8] [4, 5, 5]Invariant and Equivariant Graph Networks
- 7.00, 1.41, [8, 8, 5] [3, 5, 5]The effects of neural resource constraints on early visual representations
- 7.00, 0.82, [8, 6, 7] [4, 2, 3]Recurrent Experience Replay in Distributed Reinforcement Learning
- 7.00, 1.41, [9, 6, 6] [3, 4, 4]Padam: Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
- 7.00, 0.82, [7, 6, 8] [3, 4, 2]Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
- 7.00, 0.82, [8, 6, 7] [3, 4, 4]Quasi-hyperbolic momentum and Adam for deep learning
- 7.00, 0.00, [7, 7, 7] [3, 3, 3]The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure
- 7.00, 1.41, [8, 5, 8] [4, 2, 3]Learning Self-Imitating Diverse Policies
- 7.00, 1.41, [8, 5, 8] [4, 4, 5]Unsupervised Domain Adaptation for Distance Metric Learning
- 7.00, 0.00, [7, 7, 7] [3, 4, 4]Scalable Reversible Generative Models with Free-form Continuous Dynamics
- 7.00, 1.63, [7, 9, 5] [3, 4, 4]Feature Intertwiners
- 7.00, 0.00, [7, 7, 7] [4, 4, 4]Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching
- 7.00, 1.63, [7, 5, 9] [4, 3, 4]ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
- 7.00, 0.82, [6, 8, 7] [4, 4, 4]textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE WITH DISTRIBUTED COMPOSITIONAL PRIOR
- 7.00, 1.41, [8, 5, 8] [4, 5, 5]Local SGD Converges Fast and Communicates Little
- 7.00, 0.00, [7, 7, 7] [3, 5, 3]The role of over-parametrization in generalization of neural networks
- 7.00, 1.63, [9, 5, 7] [5, 4, 4]The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
- 7.00, 1.41, [5, 8, 8] [3, 4, 3]Don’t Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
- 7.00, 0.00, [7, 7, 7] [4, 4, 4]What do you learn from context? Probing for sentence structure in contextualized word representations
- 7.00, 0.82, [6, 7, 8] [4, 3, 4]Learning Implicitly Recurrent CNNs Through Parameter Sharing
- 7.00, 1.41, [8, 8, 5] [4, 4, 4]The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
- 7.00, 0.82, [6, 8, 7] [3, 4, 4]Learning Neural PDE Solvers with Convergence Guarantees
- 7.00, 0.82, [8, 6, 7] [4, 4, 4]Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
- 7.00, 0.00, [7, 7, 7] [3, 3, 3]Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL
- 7.00, 0.00, [7, 7, 7] [5, 3, 3]Modeling Uncertainty with Hedged Instance Embeddings
- 7.00, 0.00, [7, 7, 7] [3, 3, 3]Learning to Navigate the Web
- 7.00, 0.00, [7, 7, 7] [4, 3, 2]G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space
- 7.00, 0.82, [8, 7, 6] [3, 4, 3]GANSynth: Adversarial Neural Audio Synthesis
- 7.00, 0.82, [8, 6, 7] [4, 3, 5]K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning
- 7.00, 0.82, [8, 7, 6] [3, 2, 4]Learning sparse relational transition models
- 7.00, 0.82, [8, 6, 7] [4, 3, 4]Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
- 7.00, 0.82, [8, 6, 7] [3, 3, 3]On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks
- 7.00, 1.41, [5, 8, 8] [3, 2, 2]Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
- 6.80, 0.40, [6, 7, 7, 7, 7] [1, 3, 4, 3, 2]Subgradient Descent Learns Orthogonal Dictionaries
- 6.67, 1.25, [5, 7, 8] [3, 3, 2]Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability
- 6.67, 0.47, [6, 7, 7] [3, 4, 2]RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
- 6.67, 0.94, [8, 6, 6] [3, 3, 4]Principled Deep Neural Network Training through Linear Programming
- 6.67, 0.94, [8, 6, 6] [4, 4, 4]Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information
- 6.67, 0.47, [7, 7, 6] [4, 4, 4]Analysis of Quantized Models
- 6.67, 0.94, [8, 6, 6] [5, 3, 4]Practical lossless compression with latent variables using bits back coding
- 6.67, 0.47, [6, 7, 7] [5, 4, 4]Sample Efficient Adaptive Text-to-Speech
- 6.67, 1.25, [7, 5, 8] [3, 4, 4]Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks
- 6.67, 1.89, [8, 4, 8] [5, 4, 4]LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
- 6.67, 0.47, [7, 7, 6] [3, 3, 2]Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
- 6.67, 0.47, [6, 7, 7] [3, 3, 4]Towards the first adversarially robust neural network model on MNIST
- 6.67, 0.94, [6, 8, 6] [3, 4, 4]Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
- 6.67, 0.47, [6, 7, 7] [3, 4, 4]Meta-Learning For Stochastic Gradient MCMC
- 6.67, 0.47, [7, 6, 7] [3, 3, 3]Trellis Networks for Sequence Modeling
- 6.67, 0.47, [6, 7, 7] [3, 3, 4]ADef: an Iterative Algorithm to Construct Adversarial Deformations
- 6.67, 0.47, [7, 6, 7] [3, 2, 4]Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
- 6.67, 0.94, [6, 8, 6] [2, 5, 3]Learning to Schedule Communication in Multi-agent Reinforcement Learning
- 6.67, 0.47, [6, 7, 7] [3, 2, 3]Learning Factorized Multimodal Representations
- 6.67, 0.94, [8, 6, 6] [4, 5, 4]Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach
- 6.67, 1.70, [6, 5, 9] [4, 2, 4]Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series
- 6.67, 0.47, [7, 6, 7] [3, 4, 3]Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
- 6.67, 0.47, [7, 7, 6] [4, 5, 5]Hyperbolic Attention Networks
- 6.67, 1.25, [7, 5, 8] [3, 3, 3]NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
- 6.67, 0.47, [7, 7, 6] [4, 2, 3]Latent Convolutional Models
- 6.67, 1.25, [8, 7, 5] [4, 4, 3]Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces
- 6.67, 0.47, [7, 7, 6] [4, 3, 4]Generalized Tensor Models for Recurrent Neural Networks
- 6.67, 0.94, [6, 8, 6] [2, 4, 4]Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods
- 6.67, 0.94, [8, 6, 6] [3, 4, 4]Episodic Curiosity through Reachability
- 6.67, 0.47, [6, 7, 7] [3, 4, 4]Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer
- 6.67, 0.47, [7, 6, 7] [3, 4, 4]Solving the Rubik’s Cube with Approximate Policy Iteration
- 6.67, 0.47, [7, 6, 7] [4, 5, 3]Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach
- 6.67, 0.94, [6, 8, 6] [3, 4, 3]Unsupervised Learning via Meta-Learning
- 6.67, 0.94, [6, 6, 8] [2, 2, 2]Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
- 6.67, 0.94, [6, 6, 8] [3, 2, 4]A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery
- 6.67, 0.47, [7, 6, 7] [4, 5, 4]Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
- 6.67, 1.25, [8, 5, 7] [4, 4, 4]No Training Required: Exploring Random Encoders for Sentence Classification
- 6.67, 0.47, [7, 6, 7] [5, 4, 4]Learning Two-layer Neural Networks with Symmetric Inputs
- 6.67, 0.47, [7, 7, 6] [4, 4, 4]Phase-Aware Speech Enhancement with Deep Complex U-Net
- 6.67, 1.25, [8, 5, 7] [1, 5, 4]Deep Layers as Stochastic Solvers
- 6.67, 0.47, [7, 6, 7] [4, 4, 4]Graph HyperNetworks for Neural Architecture Search
- 6.67, 1.25, [7, 8, 5] [4, 4, 4]Complement Objective Training
- 6.67, 0.47, [7, 6, 7] [4, 4, 3]Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
- 6.67, 0.94, [8, 6, 6] [4, 4, 4]Generative Question Answering: Learning to Answer the Whole Question
- 6.67, 1.70, [6, 9, 5] [3, 4, 4]Detecting Adversarial Examples Via Neural Fingerprinting
- 6.67, 1.25, [8, 5, 7] [4, 3, 1]Learning concise representations for regression by evolving networks of trees
- 6.67, 0.47, [6, 7, 7] [3, 4, 4]Optimal Completion Distillation for Sequence Learning
- 6.67, 1.70, [9, 5, 6] [4, 4, 4]AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods
- 6.67, 0.47, [7, 6, 7] [4, 3, 1]Visual Semantic Navigation using Scene Priors
- 6.67, 0.47, [7, 7, 6] [3, 5, 4]Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
- 6.67, 0.47, [6, 7, 7] [4, 5, 4]A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
- 6.67, 0.47, [6, 7, 7] [4, 4, 4]Adversarial Attacks on Graph Neural Networks via Meta Learning
- 6.67, 0.47, [6, 7, 7] [4, 5, 3]Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
- 6.67, 0.47, [7, 7, 6] [4, 4, 4]Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
- 6.67, 0.47, [7, 7, 6] [5, 4, 4]Three Mechanisms of Weight Decay Regularization
- 6.67, 0.47, [6, 7, 7] [4, 2, 2]Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
- 6.67, 0.94, [6, 8, 6] [3, 4, 4]Transferring Knowledge across Learning Processes
- 6.67, 1.70, [9, 5, 6] [3, 4, 1]Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
- 6.67, 0.47, [7, 6, 7] [2, 3, 4]Defensive Quantization: When Efficiency Meets Robustness
- 6.67, 1.25, [7, 8, 5] [3, 4, 5]Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
- 6.67, 0.47, [7, 6, 7] [4, 4, 5]FlowQA: Grasping Flow in History for Conversational Machine Comprehension
- 6.67, 0.94, [6, 6, 8] [4, 5, 4]Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion
- 6.67, 1.25, [8, 5, 7] [4, 2, 1]GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
- 6.67, 0.47, [7, 7, 6] [3, 3, 2]Automatically Composing Representation Transformations as a Means for Generalization
- 6.67, 0.47, [6, 7, 7] [4, 4, 3]RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
- 6.67, 1.25, [8, 7, 5] [4, 4, 3]Looking for ELMo’s friends: Sentence-Level Pretraining Beyond Language Modeling
- 6.67, 0.47, [7, 6, 7] [3, 1, 3]A Mean Field Theory of Batch Normalization
- 6.67, 1.25, [5, 7, 8] [3, 3, 4]Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
- 6.67, 0.47, [7, 6, 7] [4, 3, 4]Active Learning with Partial Feedback
- 6.67, 0.47, [7, 6, 7] [4, 5, 4]Learning from Incomplete Data with Generative Adversarial Networks
- 6.67, 0.47, [7, 6, 7] [3, 4, 4]Do Deep Generative Models Know What They Don’t Know?
- 6.67, 0.94, [6, 8, 6] [4, 4, 4]RelGAN: Relational Generative Adversarial Networks for Text Generation
- 6.67, 0.47, [7, 6, 7] [2, 2, 2]Provable Online Dictionary Learning and Sparse Coding
- 6.67, 0.94, [6, 6, 8] [4, 4, 4]Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions
- 6.67, 0.47, [7, 7, 6] [4, 5, 5]SPIGAN: Privileged Adversarial Learning from Simulation
- 6.67, 0.47, [7, 6, 7] [4, 3, 4]Disjoint Mapping Network for Cross-modal Matching of Voices and Faces
- 6.67, 0.47, [7, 7, 6] [4, 5, 4]Learning to Infer and Execute 3D Shape Programs
- 6.67, 1.89, [8, 4, 8] [4, 4, 4]A Generative Model For Electron Paths
- 6.67, 0.94, [6, 6, 8] [3, 2, 4]Stochastic Optimization of Sorting Networks via Continuous Relaxations
- 6.67, 0.47, [7, 6, 7] [2, 5, 4]Learning a Meta-Solver for Syntax-Guided Program Synthesis
- 6.67, 0.94, [6, 8, 6] [1, 4, 4]There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
- 6.67, 1.25, [7, 8, 5] [5, 4, 5]Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference
- 6.50, 1.50, [4, 7, 7, 8] [3, 3, 2, 5]Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
- 6.33, 0.94, [7, 5, 7] [3, 5, 3]Stochastic Gradient Descent Learns State Equations with Nonlinear Activations
- 6.33, 0.47, [6, 6, 7] [4, 3, 3]Improved Gradient Estimators for Stochastic Discrete Variables
- 6.33, 1.70, [7, 4, 8] [3, 5, 5]Learning Preconditioner on Matrix Lie Group
- 6.33, 1.25, [8, 5, 6] [4, 4, 4]Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
- 6.33, 0.47, [7, 6, 6] [5, 4, 2]Local Critic Training of Deep Neural Networks
- 6.33, 1.70, [4, 8, 7] [4, 4, 4]Are adversarial examples inevitable?
- 6.33, 1.25, [6, 8, 5] [4, 4, 4]Generating Multiple Objects at Spatially Distinct Locations
- 6.33, 0.47, [6, 6, 7] [4, 4, 3]DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION FOR CONVOLUTIONAL NETWORKS
- 6.33, 1.25, [6, 5, 8] [2, 5, 3]signSGD via Zeroth-Order Oracle
- 6.33, 0.47, [6, 7, 6] [2, 2, 4]Reward Constrained Policy Optimization
- 6.33, 1.25, [5, 6, 8] [5, 5, 4]Quaternion Recurrent Neural Networks
- 6.33, 0.94, [5, 7, 7] [3, 3, 4]DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
- 6.33, 1.89, [5, 5, 9] [4, 3, 5]Laplacian Networks: Bounding Indicator Function Smoothness for Neural Networks Robustness
- 6.33, 0.94, [5, 7, 7] [4, 4, 5]Why do deep convolutional networks generalize so poorly to small image transformations?
- 6.33, 1.25, [6, 8, 5] [4, 3, 3]Hierarchical Visuomotor Control of Humanoids
- 6.33, 0.94, [7, 5, 7] [4, 4, 4]Hindsight policy gradients
- 6.33, 0.47, [7, 6, 6] [4, 4, 4]Attentive Neural Processes
- 6.33, 0.94, [7, 5, 7] [5, 5, 4]ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS
- 6.33, 0.94, [7, 5, 7] [5, 4, 2]Execution-Guided Neural Program Synthesis
- 6.33, 0.47, [7, 6, 6] [4, 5, 3]Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration
- 6.33, 1.25, [5, 8, 6] [4, 3, 3]Learning Recurrent Binary/Ternary Weights
- 6.33, 0.47, [7, 6, 6] [5, 5, 5]Attention, Learn to Solve Routing Problems!
- 6.33, 0.94, [5, 7, 7] [4, 3, 3]Improving Generalization and Stability of Generative Adversarial Networks
- 6.33, 0.47, [7, 6, 6] [5, 4, 4]Visceral Machines: Reinforcement Learning with Intrinsic Physiological Rewards
- 6.33, 0.47, [6, 6, 7] [4, 3, 3]Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications
- 6.33, 0.94, [5, 7, 7] [4, 2, 3]L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data
- 6.33, 0.94, [7, 7, 5] [4, 3, 4]Deep reinforcement learning with relational inductive biases
- 6.33, 0.47, [6, 7, 6] [4, 4, 4]GO Gradient for Expectation-Based Objectives
- 6.33, 0.94, [7, 5, 7] [3, 4, 4]PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees
- 6.33, 0.94, [7, 5, 7] [2, 3, 4]Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
- 6.33, 1.25, [6, 8, 5] [5, 5, 4]Multi-Domain Adversarial Learning
- 6.33, 0.47, [6, 7, 6] [5, 5, 2]Improving MMD-GAN Training with Repulsive Loss Function
- 6.33, 0.47, [6, 6, 7] [4, 3, 4]FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS
- 6.33, 1.25, [5, 6, 8] [4, 4, 4]Autoencoder-based Music Translation
- 6.33, 1.25, [6, 5, 8] [3, 4, 5]Fluctuation-dissipation relations for stochastic gradient descent
- 6.33, 0.47, [7, 6, 6] [3, 4, 4]Adaptive Estimators Show Information Compression in Deep Neural Networks
- 6.33, 1.25, [5, 8, 6] [5, 4, 4]On the loss landscape of a class of deep neural networks with no bad local valleys
- 6.33, 0.47, [6, 7, 6] [4, 4, 3]Multilingual Neural Machine Translation with Knowledge Distillation
- 6.33, 0.94, [5, 7, 7] [3, 3, 3]Emergent Coordination Through Competition
- 6.33, 1.70, [7, 8, 4] [4, 5, 3]Knowledge Flow: Improve Upon Your Teachers
- 6.33, 0.94, [5, 7, 7] [4, 3, 3]Representation Degeneration Problem in Training Natural Language Generation Models
- 6.33, 0.47, [6, 7, 6] [4, 4, 4]SNAS: stochastic neural architecture search
- 6.33, 0.47, [6, 6, 7] [3, 4, 2]Understanding Composition of Word Embeddings via Tensor Decomposition
- 6.33, 1.25, [6, 5, 8] [4, 4, 5]Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
- 6.33, 0.47, [6, 6, 7] [4, 4, 4]RNNs implicitly implement tensor-product representations
- 6.33, 0.94, [5, 7, 7] [2, 3, 2]STRUCTURED ADVERSARIAL ATTACK: TOWARDS GENERAL IMPLEMENTATION AND BETTER INTERPRETABILITY
- 6.33, 0.94, [7, 7, 5] [3, 4, 5]Learning deep representations by mutual information estimation and maximization
- 6.33, 0.47, [7, 6, 6] [3, 4, 3]Bayesian Policy Optimization for Model Uncertainty
- 6.33, 1.25, [6, 8, 5] [3, 5, 3]A NOVEL VARIATIONAL FAMILY FOR HIDDEN NON-LINEAR MARKOV MODELS
- 6.33, 0.47, [7, 6, 6] [5, 4, 3]From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference
- 6.33, 0.47, [6, 6, 7] [4, 3, 4]Discriminator Rejection Sampling
- 6.33, 0.47, [6, 7, 6] [5, 5, 5]AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
- 6.33, 0.94, [7, 5, 7] [4, 3, 3]The Laplacian in RL: Learning Representations with Efficient Approximations
- 6.33, 0.47, [7, 6, 6] [4, 2, 4]On Computation and Generalization of Generative Adversarial Networks under Spectrum Control
- 6.33, 0.47, [7, 6, 6] [5, 3, 3]Learning Finite State Representations of Recurrent Policy Networks
- 6.33, 0.47, [7, 6, 6] [2, 3, 5]Analyzing Inverse Problems with Invertible Neural Networks
- 6.33, 0.94, [7, 5, 7] [4, 5, 4]On Self Modulation for Generative Adversarial Networks
- 6.33, 0.94, [5, 7, 7] [5, 3, 4]Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation
- 6.33, 0.47, [7, 6, 6] [4, 2, 4]Universal Transformers
- 6.33, 0.47, [7, 6, 6] [5, 3, 4]Variational Autoencoders with Jointly Optimized Latent Dependency Structure
- 6.33, 1.25, [5, 6, 8] [4, 5, 4]Hierarchical Generative Modeling for Controllable Speech Synthesis
- 6.33, 0.47, [6, 6, 7] [3, 3, 3]Individualized Controlled Continuous Communication Model for Multiagent Cooperative and Competitive Tasks
- 6.33, 1.25, [5, 6, 8] [4, 2, 3]A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
- 6.33, 0.47, [7, 6, 6] [5, 4, 4]Instance-aware Image-to-Image Translation
- 6.33, 1.70, [7, 8, 4] [3, 4, 4]The Deep Weight Prior
- 6.33, 1.70, [8, 4, 7] [4, 4, 4]Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
- 6.33, 1.89, [9, 5, 5] [5, 4, 4]From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
- 6.33, 0.47, [6, 7, 6] [4, 4, 4]Empirical Bounds on Linear Regions of Deep Rectifier Networks
- 6.33, 0.47, [7, 6, 6] [4, 4, 5]Multilingual Neural Machine Translation With Soft Decoupled Encoding
- 6.33, 0.47, [6, 7, 6] [3, 2, 3]On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization
- 6.33, 0.47, [6, 6, 7] [4, 4, 5]MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
- 6.33, 1.70, [7, 8, 4] [3, 4, 3]CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild
- 6.33, 1.25, [5, 6, 8] [4, 3, 4]BNN+: Improved Binary Network Training
- 6.33, 1.70, [8, 7, 4] [3, 4, 5]Statistical Verification of Neural Networks
- 6.33, 1.25, [8, 5, 6] [4, 5, 4]Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
- 6.33, 0.47, [6, 6, 7] [4, 4, 2]Stable Recurrent Models
- 6.33, 0.94, [7, 5, 7] [3, 5, 2]Learning Mixed-Curvature Representations in Product Spaces
- 6.33, 0.47, [6, 6, 7] [3, 3, 3]Generating Multi-Agent Trajectories using Programmatic Weak Supervision
- 6.33, 0.47, [7, 6, 6] [4, 4, 4]Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension
- 6.33, 2.05, [4, 6, 9] [4, 4, 4]BA-Net: Dense Bundle Adjustment Networks
- 6.33, 2.05, [4, 9, 6] [4, 4, 4]Variance Reduction for Reinforcement Learning in Input-Driven Environments
- 6.33, 1.89, [5, 9, 5] [4, 4, 4]Predicting the Generalization Gap in Deep Networks with Margin Distributions
- 6.33, 1.25, [6, 5, 8] [4, 5, 5]Unsupervised Control Through Non-Parametric Discriminative Rewards
- 6.33, 0.94, [7, 5, 7] [4, 5, 3]Information asymmetry in KL-regularized RL
- 6.33, 0.94, [7, 5, 7] [5, 5, 3]Diversity-Sensitive Conditional Generative Adversarial Networks
- 6.33, 0.94, [7, 5, 7] [3, 4, 3]The Unreasonable Effectiveness of (Zero) Initialization in Deep Residual Learning
- 6.33, 0.47, [6, 7, 6] [3, 4, 3]Preventing Posterior Collapse with delta-VAEs
- 6.33, 1.70, [8, 7, 4] [4, 4, 5]TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer
- 6.33, 0.47, [6, 7, 6] [5, 4, 4]Feature-Wise Bias Amplification
- 6.33, 1.25, [8, 5, 6] [5, 5, 3]Machine Translation With Weakly Paired Bilingual Documents
- 6.33, 0.94, [5, 7, 7] [4, 3, 3]Don’t let your Discriminator be fooled
- 6.33, 1.89, [5, 5, 9] [4, 3, 4]Diagnosing and Enhancing VAE Models
- 6.33, 0.47, [7, 6, 6] [5, 3, 3]Spherical CNNs on Unstructured Grids
- 6.33, 2.05, [6, 9, 4] [5, 4, 5]Toward Understanding the Impact of Staleness in Distributed Machine Learning
- 6.33, 0.94, [7, 5, 7] [2, 4, 3]On the Sensitivity of Adversarial Robustness to Input Data Distributions
- 6.33, 1.89, [9, 5, 5] [4, 4, 5]Reasoning About Physical Interactions with Object-Centric Models
- 6.33, 0.47, [6, 6, 7] [3, 4, 3]Multiple-Attribute Text Rewriting
- 6.33, 1.25, [6, 8, 5] [4, 4, 4]Neural Graph Evolution: Automatic Robot Design
- 6.33, 0.47, [6, 7, 6] [4, 3, 4]Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
- 6.33, 1.25, [8, 6, 5] [4, 5, 4]DyRep: Learning Representations over Dynamic Graphs
- 6.33, 0.47, [6, 6, 7] [4, 4, 5]Eidetic 3D LSTM: A Model for Video Prediction and Beyond
- 6.33, 1.70, [7, 4, 8] [3, 3, 5]Probabilistic Neural-Symbolic Models for Interpretable Visual Question Answering
- 6.33, 0.94, [5, 7, 7] [4, 2, 3]The Limitations of Adversarial Training and the Blind-Spot Attack
- 6.33, 0.47, [6, 6, 7] [4, 4, 3]Regularized Learning for Domain Adaptation under Label Shifts
- 6.25, 0.83, [7, 7, 5, 6] [4, 1, 4, 4]Towards Consistent Performance on Atari using Expert Demonstrations
- 6.25, 0.83, [5, 7, 7, 6] [3, 3, 4, 5]Learning Protein Structure with a Differentiable Simulator
- 6.25, 0.83, [7, 5, 7, 6] [4, 3, 3, 4]The Implicit Preference Information in an Initial State
- 6.25, 0.83, [7, 6, 7, 5] [5, 4, 4, 4]Competitive experience replay
- 6.25, 1.09, [8, 6, 6, 5] [3, 3, 2, 4]Efficiently testing local optimality and escaping saddles for ReLU networks
- 6.25, 1.92, [7, 3, 8, 7] [1, 4, 1, 4]DISTRIBUTIONAL CONCAVITY REGULARIZATION FOR GANS
- 6.00, 0.82, [7, 6, 5] [3, 4, 3]Invariance and Inverse Stability under ReLU
- 6.00, 0.82, [5, 7, 6] [3, 4, 5]Precision Highway for Ultra Low-precision Quantization
- 6.00, 0.82, [7, 5, 6] [4, 3, 5]Large Scale Graph Learning From Smooth Signals
- 6.00, 0.82, [5, 6, 7] [3, 4, 4]L2-Nonexpansive Neural Networks
- 6.00, 0.82, [6, 7, 5] [4, 3, 4]Adversarial Imitation via Variational Inverse Reinforcement Learning
- 6.00, 1.41, [7, 4, 7] [3, 4, 3]Monge-Amp\`ere Flow for Generative Modeling
- 6.00, 0.00, [6, 6, 6] [3, 4, 3]INVASE: Instance-wise Variable Selection using Neural Networks
- 6.00, 0.82, [6, 5, 7] [4, 5, 4]DPSNet: End-to-end Deep Plane Sweep Stereo
- 6.00, 2.45, [6, 3, 9] [3, 4, 2]SUPERVISED POLICY UPDATE
- 6.00, 0.00, [6, 6, 6] [4, 5, 4]DATNet: Dual Adversarial Transfer for Low-resource Named Entity Recognition
- 6.00, 2.16, [8, 3, 7] [3, 4, 4]A rotation-equivariant convolutional neural network model of primary visual cortex
- 6.00, 1.41, [7, 7, 4] [4, 4, 4]ANYTIME MINIBATCH: EXPLOITING STRAGGLERS IN ONLINE DISTRIBUTED OPTIMIZATION
- 6.00, 0.71, [6, 6, 7, 5] [2, 3, 2, 4]Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection
- 6.00, 0.00, [6, 6, 6] [3, 3, 3]Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings
- 6.00, 0.00, [6, 6, 6] [4, 4, 3]Variance Networks: When Expectation Does Not Meet Your Expectations
- 6.00, 0.82, [7, 5, 6] [4, 4, 4]Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis
- 6.00, 0.82, [6, 7, 5] [4, 4, 3]On Tighter Generalization Bounds for Deep Neural Networks: CNNs, ResNets, and Beyond
- 6.00, 1.63, [4, 6, 8] [4, 3, 5]Formal Limitations on the Measurement of Mutual Information
- 6.00, 0.00, [6, 6, 6] [4, 5, 3]Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers
- 6.00, 0.82, [7, 5, 6] [3, 5, 4]Dirichlet Variational Autoencoder
- 6.00, 1.41, [8, 5, 5] [4, 2, 4]Learning Kolmogorov Models for Binary Random Variables
- 6.00, 1.41, [4, 7, 7] [3, 5, 4]Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
- 6.00, 1.63, [8, 4, 6] [3, 5, 3]Are Generative Classifiers More Robust to Adversarial Attacks?
- 6.00, 0.82, [7, 6, 5] [3, 3, 4]EFFICIENT TWO-STEP ADVERSARIAL DEFENSE FOR DEEP NEURAL NETWORKS
- 6.00, 0.82, [5, 6, 7] [4, 4, 3]POLICY GENERALIZATION IN CAPACITY-LIMITED REINFORCEMENT LEARNING
- 6.00, 1.87, [5, 9, 4, 6] [5, 4, 5, 4]Adversarial Vulnerability of Neural Networks Increases with Input Dimension
- 6.00, 1.41, [7, 7, 4] [2, 3, 4]GamePad: A Learning Environment for Theorem Proving
- 6.00, 1.41, [7, 4, 7] [3, 5, 4]The Singular Values of Convolutional Layers
- 6.00, 1.41, [5, 8, 5] [4, 4, 4]code2seq: Generating Sequences from Structured Representations of Code
- 6.00, 1.00, [5, 7] [5, 4]PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
- 6.00, 1.63, [8, 4, 6] [3, 4, 4]Manifold Mixup: Learning Better Representations by Interpolating Hidden States
- 6.00, 0.82, [7, 5, 6] [5, 5, 3]Temporal Gaussian Mixture Layer for Videos
- 6.00, 0.82, [7, 5, 6] [4, 4, 5]Neural Speed Reading with Structural-Jump-LSTM
- 6.00, 0.82, [6, 7, 5] [4, 3, 3]Information Theoretic lower bounds on negative log likelihood
- 6.00, 0.71, [7, 6, 6, 5] [3, 3, 3, 4]Sinkhorn AutoEncoders
- 6.00, 0.00, [6, 6, 6] [4, 4, 2]Neural Networks for Modeling Source Code Edits
- 6.00, 0.82, [6, 5, 7] [4, 3, 4]LayoutGAN: Generating Graphic Layouts with Wireframe Discriminator
- 6.00, 0.82, [7, 5, 6] [4, 5, 4]SGD Converges to Global Minimum in Deep Learning via Star-convex Path
- 6.00, 0.82, [5, 6, 7] [4, 4, 2]Learning from Positive and Unlabeled Data with a Selection Bias
- 6.00, 0.82, [5, 6, 7] [4, 3, 3]Aggregated Momentum: Stability Through Passive Damping
- 6.00, 1.63, [6, 8, 4] [4, 4, 4]ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.
- 6.00, 0.00, [6, 6, 6] [4, 4, 3]Countering Language Drift via Grounding
- 6.00, 0.00, [6, 6, 6] [4, 4, 4]Measuring Compositionality in Representation Learning
- 6.00, 2.16, [9, 5, 4] [5, 4, 4]A Biologically Inspired Visual Working Memory for Deep Networks
- 6.00, 0.82, [6, 5, 7] [4, 2, 3]Universal Successor Features Approximators
- 6.00, 1.41, [5, 8, 5] [4, 3, 5]Deep Convolutional Networks as shallow Gaussian Processes
- 6.00, 0.00, [6, 6, 6] [4, 3, 4]Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks
- 6.00, 0.82, [7, 5, 6] [1, 3, 3]Variational Bayesian Phylogenetic Inference
- 6.00, 0.71, [5, 6, 6, 7] [4, 3, 3, 3]Relational Forward Models for Multi-Agent Learning
- 6.00, 0.82, [7, 5, 6] [4, 5, 3]Generative predecessor models for sample-efficient imitation learning
- 6.00, 0.82, [5, 6, 7] [5, 5, 3]Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile
- 6.00, 0.00, [6, 6, 6] [1, 2, 4]Stable Opponent Shaping in Differentiable Games
- 6.00, 0.82, [7, 6, 5] [4, 4, 4]DeepOBS: A Deep Learning Optimizer Benchmark Suite
- 6.00, 0.82, [6, 7, 5] [4, 4, 4]Policy Transfer with Strategy Optimization
- 6.00, 1.41, [4, 7, 7] [4, 4, 4]Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder
- 6.00, 0.82, [7, 6, 5] [2, 4, 3]Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems
- 6.00, 1.63, [8, 4, 6] [3, 4, 3]Integer Networks for Data Compression with Latent-Variable Models
- 6.00, 0.82, [6, 5, 7] [3, 3, 5]Residual Non-local Attention Networks for Image Restoration
- 6.00, 0.00, [6, 6, 6] [4, 3, 4]Information-Directed Exploration for Deep Reinforcement Learning
- 6.00, 0.82, [5, 7, 6] [5, 4, 4]Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs
- 6.00, 1.87, [7, 8, 6, 3] [4, 4, 4, 5]Gradient Descent Provably Optimizes Over-parameterized Neural Networks
- 6.00, 1.41, [4, 7, 7] [4, 3, 3]Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
- 6.00, 1.22, [4, 7, 6, 7] [5, 4, 3, 5]Dynamic Channel Pruning: Feature Boosting and Suppression
- 6.00, 0.82, [7, 6, 5] [3, 4, 3]Unsupervised Hyper-alignment for Multilingual Word Embeddings
- 6.00, 0.00, [6, 6, 6] [3, 4, 5]GraphSeq2Seq: Graph-Sequence-to-Sequence for Neural Machine Translation
- 6.00, 0.82, [5, 7, 6] [4, 4, 3]Multi-class classification without multi-class labels
- 6.00, 1.63, [8, 6, 4] [3, 3, 4]On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
- 6.00, 0.00, [6, 6, 6] [4, 3, 4]Learning Disentangled Representations with Reference-Based Variational Autoencoders
- 6.00, 1.41, [7, 4, 7] [3, 5, 4]Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
- 6.00, 1.41, [7, 4, 7] [3, 3, 4]AutoLoss: Learning Discrete Schedule for Alternate Optimization
- 6.00, 0.82, [6, 7, 5] [4, 4, 4]Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures
- 6.00, 0.00, [6, 6, 6] [4, 4, 4]Adversarial Information Factorization
- 6.00, 2.16, [7, 3, 8] [3, 4, 4]ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks
- 6.00, 0.00, [6, 6, 6] [4, 5, 4]BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop
- 6.00, 1.87, [6, 9, 5, 4] [3, 4, 3, 5]Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations
- 6.00, 0.82, [5, 7, 6] [4, 4, 4]Hierarchical Reinforcement Learning with Limited Policies and Hindsight
- 6.00, 2.16, [5, 9, 4] [4, 4, 4]Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
- 6.00, 2.16, [9, 4, 5] [5, 4, 4]Detecting Memorization in ReLU Networks
- 6.00, 1.63, [6, 4, 8] [4, 4, 3]DADAM: A consensus-based distributed adaptive gradient method for online optimization
- 6.00, 1.63, [4, 6, 8] [4, 3, 4]A Systematic Study of Binary Neural Networks’ Optimisation
- 6.00, 1.41, [7, 4, 7] [4, 4, 5]Graph U-Net
- 6.00, 0.82, [7, 6, 5] [4, 3, 3]LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING
- 6.00, 1.41, [5, 8, 5] [3, 4, 3]On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent
- 6.00, 1.41, [8, 5, 5] [4, 4, 4]Detecting Out-Of-Distribution Samples Using Low-Order Deep Features Statistics
- 6.00, 0.82, [5, 7, 6] [4, 4, 4]Decoupled Weight Decay Regularization
- 6.00, 0.00, [6, 6, 6] [5, 4, 5]Diversity and Depth in Per-Example Routing Models
- 6.00, 1.41, [5, 5, 8] [4, 4, 4]ProxQuant: Quantized Neural Networks via Proximal Operators
- 6.00, 0.00, [6, 6, 6] [4, 3, 4]Wasserstein Barycenter Model Ensembling
- 6.00, 0.00, [6, 6, 6] [3, 4, 3]Stochastic Gradient Push for Distributed Deep Learning
- 6.00, 0.82, [5, 7, 6] [3, 1, 3]DOM-Q-NET: Grounded RL on Structured Language
- 6.00, 1.41, [8, 5, 5] [5, 3, 5]Meta-Learning with Latent Embedding Optimization
- 6.00, 0.00, [6, 6, 6] [3, 3, 4]Reinforcement Learning with Perturbed Rewards
- 6.00, 0.82, [5, 6, 7] [3, 3, 3]MEAN-FIELD ANALYSIS OF BATCH NORMALIZATION
- 6.00, 1.63, [8, 4, 6] [3, 4, 3]Learning what and where to attend with humans in the loop
- 6.00, 0.82, [7, 6, 5] [4, 5, 3]How to train your MAML
- 6.00, 0.82, [7, 6, 5] [4, 4, 3]Learning Heuristics for Automated Reasoning through Reinforcement Learning
- 6.00, 1.63, [4, 8, 6] [4, 3, 2]Lyapunov-based Safe Policy Optimization
- 6.00, 0.00, [6, 6, 6] [4, 3, 3]Dimension-Free Bounds for Low-Precision Training
- 6.00, 1.41, [4, 7, 7] [3, 4, 4]Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision
- 6.00, 0.00, [6, 6, 6] [4, 4, 4]Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images
- 6.00, 1.63, [4, 8, 6] [3, 4, 3]Unsupervised Adversarial Image Reconstruction
- 6.00, 0.00, [6, 6, 6] [4, 2, 3]Environment Probing Interaction Policies
- 6.00, 0.82, [5, 7, 6] [5, 2, 3]Neural Logic Machines
- 6.00, 0.00, [6, 6, 6] [3, 5, 5]Graph Transformer
- 6.00, 1.41, [5, 8, 5] [3, 2, 5]Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors
- 6.00, 0.82, [5, 6, 7] [3, 3, 4]Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
- 6.00, 0.00, [6, 6, 6] [2, 4, 3]Improving the Generalization of Adversarial Training with Domain Adaptation
- 6.00, 1.63, [8, 6, 4] [2, 4, 4]Learning Abstract Models for Long-Horizon Exploration
- 6.00, 0.82, [6, 7, 5] [3, 3, 4]A Direct Approach to Robust Deep Learning Using Adversarial Networks
- 6.00, 0.82, [5, 7, 6] [4, 4, 3]Spreading vectors for similarity search
- 6.00, 1.63, [4, 6, 8] [4, 4, 4]Probabilistic Planning with Sequential Monte Carlo
- 6.00, 0.82, [6, 7, 5] [3, 3, 2]Recall Traces: Backtracking Models for Efficient Reinforcement Learning
- 6.00, 0.82, [6, 5, 7] [3, 3, 3]Value Propagation Networks
- 6.00, 0.00, [6, 6, 6] [4, 5, 2]A Closer Look at Few-shot Classification
- 6.00, 1.63, [4, 8, 6] [5, 3, 3]Learning to Learn with Conditional Class Dependencies
- 6.00, 0.00, [6, 6, 6] [5, 4, 5]TarMAC: Targeted Multi-Agent Communication
- 6.00, 0.82, [5, 6, 7] [5, 4, 3]A Differentiable Self-disambiguated Sense Embedding Model via Scaled Gumbel Softmax
- 6.00, 0.00, [6, 6, 6] [3, 3, 3]A MAX-AFFINE SPLINE PERSPECTIVE OF RECURRENT NEURAL NETWORKS
- 6.00, 0.00, [6, 6, 6] [3, 3, 3]Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
- 6.00, 0.82, [7, 5, 6] [4, 4, 4]Diverse Machine Translation with a Single Multinomial Latent Variable
- 6.00, 0.00, [6, 6, 6] [2, 4, 4]Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
- 6.00, 0.00, [6, 6, 6] [3, 3, 3]Characterizing Audio Adversarial Examples Using Temporal Dependency
- 6.00, 1.63, [8, 6, 4] [5, 4, 5]Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling
- 6.00, 0.82, [6, 7, 5] [2, 2, 5]The Variational Deficiency Bottleneck
- 6.00, 0.82, [7, 6, 5] [4, 4, 4]Combinatorial Attacks on Binarized Neural Networks
- 6.00, 0.82, [5, 7, 6] [4, 2, 3]Contingency-Aware Exploration in Reinforcement Learning
- 6.00, 0.82, [7, 5, 6] [5, 4, 5]Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
- 6.00, 0.00, [6, 6, 6] [2, 2, 4]Proxy-less Architecture Search via Binarized Path Learning
- 6.00, 1.41, [4, 7, 7] [4, 4, 4]Revealing interpretable object representations from human behavior
- 6.00, 0.00, [6, 6, 6] [4, 4, 5]Multi-step Reasoning for Open-domain Question Answering
- 6.00, 0.00, [6, 6, 6] [3, 3, 4]Single Shot Neural Architecture Search Via Direct Sparse Optimization
- 5.75, 0.83, [7, 5, 6, 5] [3, 3, 3, 4]On the Spectral Bias of Neural Networks
- 5.75, 0.83, [5, 7, 6, 5] [3, 4, 3, 3]Modeling Parts, Structure, and System Dynamics via Predictive Learning
- 5.75, 0.83, [5, 6, 5, 7] [4, 4, 5, 3]An Alarm System for Segmentation Algorithm Based on Shape Model
- 5.75, 0.43, [6, 5, 6, 6] [4, 4, 3, 4]Two-Timescale Networks for Nonlinear Value Function Approximation
- 5.67, 0.94, [7, 5, 5] [5, 5, 4](Unconstrained) Beam Search is Sensitive to Large Search Discrepancies
- 5.67, 1.25, [7, 6, 4] [4, 1, 4]CONTROLLING COVARIATE SHIFT USING EQUILIBRIUM NORMALIZATION OF WEIGHTS
- 5.67, 0.47, [5, 6, 6] [4, 3, 3]Amortized Context Vector Inference for Sequence-to-Sequence Networks
- 5.67, 0.94, [5, 5, 7] [4, 5, 4]The meaning of “most” for visual question answering models
- 5.67, 2.05, [8, 3, 6] [4, 2, 3]Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm
- 5.67, 0.94, [5, 7, 5] [4, 4, 3]A unified theory of adaptive stochastic gradient descent as Bayesian filtering
- 5.67, 0.47, [5, 6, 6] [4, 4, 4]Laplacian Smoothing Gradient Descent
- 5.67, 1.25, [4, 7, 6] [4, 4, 4]Explicit Information Placement on Latent Variables using Auxiliary Generative Modelling Task
- 5.67, 1.70, [4, 5, 8] [4, 4, 4]Discriminative Active Learning
- 5.67, 1.25, [4, 7, 6] [4, 4, 3]A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit
- 5.67, 1.25, [4, 6, 7] [4, 4, 3]Generating Liquid Simulations with Deformation-aware Neural Networks
- 5.67, 0.94, [5, 7, 5] [4, 2, 5]A Kernel Random Matrix-Based Approach for Sparse PCA
- 5.67, 0.47, [6, 5, 6] [4, 4, 4]Identifying Generalization Properties in Neural Networks
- 5.67, 0.94, [5, 5, 7] [4, 4, 3]Hierarchical interpretations for neural network predictions
- 5.67, 0.47, [6, 5, 6] [4, 1, 3]Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps
- 5.67, 0.47, [6, 5, 6] [1, 3, 4]M^3RL: Mind-aware Multi-agent Management Reinforcement Learning
- 5.67, 0.47, [6, 6, 5] [4, 4, 2]Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
- 5.67, 0.47, [6, 5, 6] [3, 3, 3]Remember and Forget for Experience Replay
- 5.67, 0.94, [5, 5, 7] [4, 4, 4]Fast adversarial training for semi-supervised learning
- 5.67, 0.47, [6, 6, 5] [4, 3, 3]An Information-Theoretic Metric of Transferability for Task Transfer Learning
- 5.67, 1.25, [6, 4, 7] [4, 4, 4]Convolutional CRFs for Semantic Segmentation
- 5.67, 0.47, [6, 6, 5] [3, 5, 3]Dynamic Early Terminating of Multiply Accumulate Operations for Saving Computation Cost in Convolutional Neural Networks
- 5.67, 1.25, [4, 6, 7] [4, 4, 2]Causal importance of orientation selectivity for generalization in image recognition
- 5.67, 0.94, [5, 7, 5] [3, 3, 4]Function Space Particle Optimization for Bayesian Neural Networks
- 5.67, 0.47, [6, 5, 6] [4, 4, 4]Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
- 5.67, 1.25, [4, 7, 6] [4, 5, 4]Visual Reasoning by Progressive Module Networks
- 5.67, 0.47, [6, 6, 5] [3, 4, 3]Incremental training of multi-generative adversarial networks
- 5.67, 0.47, [6, 5, 6] [4, 3, 4]Projective Subspace Networks For Few Shot Learning
- 5.67, 0.94, [5, 7, 5] [4, 4, 3]DANA: Scalable Out-of-the-box Distributed ASGD Without Retuning
- 5.67, 1.25, [6, 7, 4] [4, 5, 4]A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation
- 5.67, 1.25, [4, 7, 6] [4, 3, 4]Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
- 5.67, 0.94, [5, 7, 5] [4, 4, 4]Cramer-Wold AutoEncoder
- 5.67, 0.47, [6, 6, 5] [5, 3, 4]Better Generalization with On-the-fly Dataset Denoising
- 5.67, 1.25, [4, 7, 6] [3, 4, 4]Talk The Walk: Navigating Grids in New York City through Grounded Dialogue
- 5.67, 0.47, [5, 6, 6] [4, 4, 4]Efficient Lifelong Learning with A-GEM
- 5.67, 0.94, [5, 5, 7] [3, 3, 5]Optimal Transport Maps For Distribution Preserving Operations on Latent Spaces of Generative Models
- 5.67, 0.94, [5, 5, 7] [4, 4, 3]Learning Implicit Generative Models by Teaching Explicit Ones
- 5.67, 1.25, [4, 7, 6] [5, 4, 3]PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning
- 5.67, 2.36, [4, 9, 4] [2, 3, 4]PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation
- 5.67, 0.47, [5, 6, 6] [5, 5, 4]State-Regularized Recurrent Networks
- 5.67, 2.36, [9, 4, 4] [4, 4, 3]The Problem of Model Completion
- 5.67, 0.47, [6, 5, 6] [4, 5, 4]Zero-Resource Multilingual Model Transfer: Learning What to Share
- 5.67, 0.94, [7, 5, 5] [4, 3, 3]Learning to Make Analogies by Contrasting Abstract Relational Structure
- 5.67, 0.47, [6, 6, 5] [2, 5, 3]Towards Understanding Regularization in Batch Normalization
- 5.67, 1.25, [6, 7, 4] [4, 4, 4]ACCELERATING NONCONVEX LEARNING VIA REPLICA EXCHANGE LANGEVIN DIFFUSION
- 5.67, 0.47, [6, 6, 5] [2, 5, 4]Identifying Bias in AI using Simulation
- 5.67, 0.47, [6, 5, 6] [3, 4, 4]Understanding GANs via Generalization Analysis for Disconnected Support
- 5.67, 0.47, [6, 5, 6] [4, 3, 3]Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior
- 5.67, 1.25, [7, 4, 6] [3, 3, 4]Guiding Physical Intuition with Neural Stethoscopes
- 5.67, 0.94, [5, 7, 5] [4, 4, 5]Whitening and Coloring transform for GANs
- 5.67, 0.47, [5, 6, 6] [3, 4, 5]Efficient Codebook and Factorization for Second Order Representation Learning
- 5.67, 0.47, [6, 6, 5] [4, 3, 5]Adversarial Attacks on Node Embeddings
- 5.67, 0.47, [6, 6, 5] [4, 4, 4]Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models
- 5.67, 0.47, [5, 6, 6] [3, 2, 3]Learning Neural Random Fields with Inclusive Auxiliary Generators
- 5.67, 0.47, [6, 6, 5] [4, 3, 3]Analysing Mathematical Reasoning Abilities of Neural Models
- 5.67, 0.47, [6, 5, 6] [4, 3, 4]Learning Representations of Sets through Optimized Permutations
- 5.67, 0.47, [6, 5, 6] [3, 4, 4]CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model
- 5.67, 0.94, [5, 7, 5] [5, 4, 3]Backprop with Approximate Activations for Memory-efficient Network Training
- 5.67, 0.94, [5, 5, 7] [3, 4, 4]Learning models for visual 3D localization with implicit mapping
- 5.67, 1.25, [4, 7, 6] [5, 4, 4]Estimating Information Flow in DNNs
- 5.67, 0.94, [5, 5, 7] [3, 3, 3]Adversarial Exploration Strategy for Self-Supervised Imitation Learning
- 5.67, 0.94, [7, 5, 5] [4, 5, 5]signSGD with Majority Vote is Communication Efficient and Byzantine Fault Tolerant
- 5.67, 0.94, [7, 5, 5] [3, 3, 3]Predicted Variables in Programming
- 5.67, 0.47, [5, 6, 6] [5, 4, 3]Stochastic Adversarial Video Prediction
- 5.67, 1.70, [4, 5, 8] [4, 4, 3]Cross-Entropy Loss Leads To Poor Margins
- 5.67, 0.47, [6, 6, 5] [4, 4, 1]Kernel Recurrent Learning (KeRL)
- 5.67, 1.25, [6, 4, 7] [5, 4, 4]Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model
- 5.67, 0.47, [6, 5, 6] [4, 5, 2]Overcoming Multi-model Forgetting
- 5.67, 0.94, [7, 5, 5] [4, 4, 4]ADAPTIVE NETWORK SPARSIFICATION VIA DEPENDENT VARIATIONAL BETA-BERNOULLI DROPOUT
- 5.67, 0.94, [5, 5, 7] [4, 5, 5]Domain Adaptation for Structured Output via Disentangled Patch Representations
- 5.67, 0.47, [6, 6, 5] [5, 2, 4]Large-Scale Answerer in Questioner’s Mind for Visual Dialog Question Generation
- 5.67, 1.25, [6, 4, 7] [4, 4, 3]Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
- 5.67, 1.25, [6, 4, 7] [2, 4, 4]Excessive Invariance Causes Adversarial Vulnerability
- 5.67, 0.47, [6, 6, 5] [4, 3, 4]Adversarial Audio Synthesis
- 5.67, 0.94, [5, 7, 5] [3, 3, 3]Spectral Inference Networks: Unifying Deep and Spectral Learning
- 5.67, 2.49, [9, 3, 5] [4, 4, 4]Unsupervised Neural Multi-Document Abstractive Summarization of Reviews
- 5.67, 1.25, [6, 4, 7] [4, 5, 4]Learning Multimodal Graph-to-Graph Translation for Molecule Optimization
- 5.67, 0.47, [6, 5, 6] [3, 4, 4]Discovery of natural language concepts in individual units
- 5.67, 0.94, [5, 5, 7] [4, 4, 4]Unsupervised Learning of Sentence Representations Using Sequence Consistency
- 5.67, 0.94, [5, 7, 5] [4, 4, 3]Improving Sequence-to-Sequence Learning via Optimal Transport
- 5.67, 1.25, [6, 4, 7] [5, 4, 3]MILE: A Multi-Level Framework for Scalable Graph Embedding
- 5.67, 1.25, [6, 4, 7] [4, 3, 3]Learning to Represent Edits
- 5.67, 0.47, [6, 6, 5] [4, 3, 3]Out-of-Sample Extrapolation with Neuron Editing
- 5.67, 0.94, [5, 5, 7] [4, 5, 4]Improving Sentence Representations with Multi-view Frameworks
- 5.67, 0.47, [6, 5, 6] [4, 3, 5]Generalizable Adversarial Training via Spectral Normalization
- 5.67, 1.89, [3, 7, 7] [4, 4, 3]Learning Entropic Wasserstein Embeddings
- 5.67, 0.47, [5, 6, 6] [2, 1, 3]Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
- 5.67, 0.47, [5, 6, 6] [5, 4, 4]Seq2Slate: Re-ranking and Slate Optimization with RNNs
- 5.67, 0.47, [5, 6, 6] [4, 4, 3]Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
- 5.67, 0.94, [7, 5, 5] [5, 3, 3]A new dog learns old tricks: RL finds classic optimization algorithms
- 5.67, 1.25, [4, 6, 7] [3, 3, 3]Variational Autoencoder with Arbitrary Conditioning
- 5.67, 0.47, [5, 6, 6] [5, 4, 4]Neural Program Repair by Jointly Learning to Localize and Repair
- 5.67, 0.94, [7, 5, 5] [4, 4, 4]Shallow Learning For Deep Networks
- 5.67, 1.25, [4, 7, 6] [4, 4, 2]Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition
- 5.67, 0.47, [6, 5, 6] [5, 4, 5]Deep Probabilistic Video Compression
- 5.67, 0.47, [6, 6, 5] [3, 4, 5]A More Globally Accurate Dimensionality Reduction Method Using Triplets
- 5.67, 1.25, [6, 4, 7] [4, 5, 4]Adaptive Gradient Methods with Dynamic Bound of Learning Rate
- 5.67, 0.47, [6, 5, 6] [2, 4, 1]Adversarially Learned Mixture Model
- 5.67, 1.25, [4, 7, 6] [4, 2, 2]Clean-Label Backdoor Attacks
- 5.67, 1.25, [7, 4, 6] [2, 4, 4]Perception-Aware Point-Based Value Iteration for Partially Observable Markov Decision Processes
- 5.67, 0.47, [5, 6, 6] [4, 3, 4]Trace-back along capsules and its application on semantic segmentation
- 5.67, 1.25, [7, 4, 6] [4, 4, 5]Hallucinations in Neural Machine Translation
- 5.67, 0.47, [5, 6, 6] [3, 1, 5]Learning Programmatically Structured Representations with Perceptor Gradients
- 5.67, 1.89, [7, 7, 3] [4, 5, 5]Learning Exploration Policies for Navigation
- 5.67, 0.94, [7, 5, 5] [3, 3, 4]Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
- 5.67, 0.94, [5, 5, 7] [4, 3, 4]Open-Ended Content-Style Recombination Via Leakage Filtering
- 5.67, 2.36, [9, 4, 4] [4, 4, 4]Bayesian Modelling and Monte Carlo Inference for GAN
- 5.67, 0.47, [6, 5, 6] [4, 3, 3]Multi-objective training of Generative Adversarial Networks with multiple discriminators
- 5.67, 1.25, [4, 7, 6] [4, 3, 3]Knowledge Representation for Reinforcement Learning using General Value Functions
- 5.67, 0.47, [6, 6, 5] [5, 5, 3]Super-Resolution via Conditional Implicit Maximum Likelihood Estimation
- 5.67, 1.25, [7, 6, 4] [4, 4, 4]CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
- 5.67, 1.25, [7, 4, 6] [4, 3, 5]NECST: Neural Joint Source-Channel Coding
- 5.67, 0.94, [7, 5, 5] [4, 3, 4]Nested Dithered Quantization for Communication Reduction in Distributed Training
- 5.67, 0.94, [5, 7, 5] [5, 3, 4]Explaining Image Classifiers by Counterfactual Generation
- 5.67, 1.25, [6, 7, 4] [5, 4, 4]The Expressive Power of Deep Neural Networks with Circulant Matrices
- 5.67, 0.47, [6, 5, 6] [3, 4, 4]Learning what you can do before doing anything
- 5.67, 0.47, [6, 6, 5] [4, 4, 4]Language Model Pre-training for Hierarchical Document Representations
- 5.67, 1.25, [6, 7, 4] [3, 4, 4]Efficient Augmentation via Data Subsampling
- 5.67, 0.47, [5, 6, 6] [4, 3, 4]Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
- 5.67, 0.94, [7, 5, 5] [4, 4, 4]Hierarchically-Structured Variational Autoencoders for Long Text Generation
- 5.67, 0.94, [5, 5, 7] [3, 4, 4]Where Off-Policy Deep Reinforcement Learning Fails
- 5.67, 1.25, [4, 7, 6] [4, 4, 4]TENSOR RING NETS ADAPTED DEEP MULTI-TASK LEARNING
- 5.67, 0.47, [6, 5, 6] [3, 5, 4]A Variational Dirichlet Framework for Out-of-Distribution Detection
- 5.67, 0.94, [5, 7, 5] [4, 3, 4]Adaptive Sample-space & Adaptive Probability coding: a neural-network based approach for compression
- 5.67, 1.70, [5, 8, 4] [4, 3, 4]Augment your batch: better training with larger batches
- 5.67, 0.94, [5, 7, 5] [4, 2, 5]On Difficulties of Probability Distillation
- 5.67, 0.47, [5, 6, 6] [2, 4, 3]Top-Down Neural Model For Formulae
- 5.67, 0.94, [5, 5, 7] [4, 4, 4]Manifold regularization with GANs for semi-supervised learning
- 5.67, 0.94, [5, 5, 7] [5, 3, 4]Cross-Task Knowledge Transfer for Visually-Grounded Navigation
- 5.67, 1.25, [6, 7, 4] [3, 2, 4]Rotation Equivariant Networks via Conic Convolution and the DFT
- 5.67, 1.89, [3, 7, 7] [5, 4, 5]Small steps and giant leaps: Minimal Newton solvers for Deep Learning
- 5.67, 1.25, [7, 4, 6] [4, 3, 4]Beyond Greedy Ranking: Slate Optimization via List-CVAE
- 5.67, 0.47, [5, 6, 6] [3, 4, 3]Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution
- 5.67, 0.47, [5, 6, 6] [4, 4, 4]Learning to Augment Influential Data
- 5.67, 1.25, [7, 4, 6] [3, 3, 3]Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference
- 5.67, 1.70, [8, 5, 4] [3, 3, 4]Cost-Sensitive Robustness against Adversarial Examples
- 5.67, 0.47, [6, 6, 5] [4, 1, 4]Learning to Design RNA
- 5.67, 1.25, [7, 4, 6] [3, 4, 3]Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure
- 5.67, 0.94, [5, 5, 7] [3, 3, 3]Finite Automata Can be Linearly Decoded from Language-Recognizing RNNs
- 5.67, 0.47, [5, 6, 6] [5, 4, 4]Selfless Sequential Learning
- 5.67, 0.47, [6, 6, 5] [4, 4, 4]Modeling the Long Term Future in Model-Based Reinforcement Learning
- 5.67, 1.25, [7, 4, 6] [3, 4, 4]Poincare Glove: Hyperbolic Word Embeddings
- 5.67, 0.47, [6, 5, 6] [5, 5, 4]Rethinking the Value of Network Pruning
- 5.67, 0.94, [5, 5, 7] [2, 4, 4]DL2: Training and Querying Neural Networks with Logic
- 5.50, 1.12, [7, 5, 4, 6] [4, 4, 4, 4]Computing committor functions for the study of rare events using deep learning with importance sampling
- 5.50, 0.50, [5, 6, 6, 5] [4, 4, 3, 4]Interactive Agent Modeling by Learning to Probe
- 5.50, 0.87, [6, 6, 6, 4] [2, 2, 3, 4]Multi-way Encoding for Robustness to Adversarial Attacks
- 5.50, 0.87, [7, 5, 5, 5] [3, 3, 4, 4]On the Margin Theory of Feedforward Neural Networks
- 5.50, 0.87, [6, 6, 6, 4] [2, 2, 4, 5]CAML: Fast Context Adaptation via Meta-Learning
- 5.50, 0.50, [5, 6] [3, 2]Policy Optimization via Stochastic Recursive Gradient Algorithm
- 5.33, 0.47, [6, 5, 5] [3, 4, 3]The Universal Approximation Power of Finite-Width Deep ReLU Networks
- 5.33, 0.47, [5, 6, 5] [3, 3, 5]Classification from Positive, Unlabeled and Biased Negative Data
- 5.33, 1.25, [4, 7, 5] [3, 4, 3]Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation
- 5.33, 0.47, [5, 6, 5] [3, 4, 4]Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
- 5.33, 0.94, [6, 6, 4] [4, 2, 3]Lipschitz regularized Deep Neural Networks converge and generalize
- 5.33, 0.47, [5, 5, 6] [3, 4, 1]Playing the Game of Universal Adversarial Perturbations
- 5.33, 0.94, [4, 6, 6] [4, 3, 3]Provable Guarantees on Learning Hierarchical Generative Models with Deep CNNs
- 5.33, 2.49, [6, 8, 2] [4, 4, 4]Caveats for information bottleneck in deterministic scenarios
- 5.33, 1.25, [5, 4, 7] [4, 4, 4]Clinical Risk: wavelet reconstruction networks for marked point processes
- 5.33, 1.70, [7, 6, 3] [3, 4, 2] The relativistic discriminator: a key element missing from standard GAN
- 5.33, 0.47, [5, 6, 5] [4, 3, 5]On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
- 5.33, 0.47, [5, 5, 6] [4, 2, 4]Adaptive Pruning of Neural Language Models for Mobile Devices
- 5.33, 0.94, [6, 4, 6] [3, 4, 4]Reducing Overconfident Errors outside the Known Distribution
- 5.33, 0.94, [6, 4, 6] [4, 5, 4]Learning to Understand Goal Specifications by Modelling Reward
- 5.33, 0.94, [6, 4, 6] [3, 4, 5]LEARNING FACTORIZED REPRESENTATIONS FOR OPEN-SET DOMAIN ADAPTATION
- 5.33, 0.47, [5, 5, 6] [4, 4, 4]SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels
- 5.33, 0.47, [5, 5, 6] [2, 4, 3]An experimental study of layer-level training speed and its impact on generalization
- 5.33, 0.47, [6, 5, 5] [4, 4, 3]Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
- 5.33, 1.89, [4, 4, 8] [4, 4, 3]DecayNet: A Study on the Cell States of Long Short Term Memories
- 5.33, 0.47, [5, 5, 6] [3, 4, 3]Training generative latent models by variational f-divergence minimization
- 5.33, 1.25, [5, 7, 4] [4, 5, 5]Domain Generalization via Invariant Representation under Domain-Class Dependency
- 5.33, 1.25, [5, 7, 4] [4, 3, 4]Distribution-Interpolation Trade off in Generative Models
- 5.33, 0.47, [5, 6, 5] [5, 2, 3]Purchase as Reward : Session-based Recommendation by Imagination Reconstruction
- 5.33, 0.47, [6, 5, 5] [3, 3, 4]Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective
- 5.33, 0.94, [6, 6, 4] [3, 3, 2]Exploring and Enhancing the Transferability of Adversarial Examples
- 5.33, 1.25, [7, 4, 5] [5, 4, 3]Switching Linear Dynamics for Variational Bayes Filtering
- 5.33, 1.25, [5, 7, 4] [4, 3, 4]The loss landscape of overparameterized neural networks
- 5.33, 0.94, [6, 4, 6] [4, 4, 3]Curiosity-Driven Experience Prioritization via Density Estimation
- 5.33, 0.47, [5, 5, 6] [5, 5, 3]Generalization and Regularization in DQN
- 5.33, 1.25, [4, 5, 7] [3, 5, 2]Invariant-equivariant representation learning for multi-class data
- 5.33, 2.62, [9, 3, 4] [4, 5, 4] Large-Scale Visual Speech Recognition
- 5.33, 0.47, [5, 5, 6] [4, 4, 4]RoC-GAN: Robust Conditional GAN
- 5.33, 1.25, [7, 5, 4] [2, 2, 2]On the Turing Completeness of Modern Neural Network Architectures
- 5.33, 0.47, [5, 6, 5] [4, 4, 2]The Unusual Effectiveness of Averaging in GAN Training
- 5.33, 0.94, [6, 6, 4] [4, 5, 4]Graph Wavelet Neural Network
- 5.33, 0.47, [6, 5, 5] [4, 4, 5]Gaussian-gated LSTM: Improved convergence by reducing state updates
- 5.33, 0.94, [6, 4, 6] [3, 3, 3]Meta Domain Adaptation: Meta-Learning for Few-Shot Learning under Domain Shift
- 5.33, 0.47, [5, 5, 6] [4, 4, 5]Learning to encode spatial relations from natural language
- 5.33, 0.47, [6, 5, 5] [3, 3, 3]Skip-gram word embeddings in hyperbolic space
- 5.33, 0.47, [6, 5, 5] [4, 4, 4]Graph Matching Networks for Learning the Similarity of Graph Structured Objects
- 5.33, 1.25, [5, 7, 4] [4, 4, 3]Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation
- 5.33, 1.70, [7, 3, 6] [4, 5, 4]Learning From the Experience of Others: Approximate Empirical Bayes in Neural Networks
- 5.33, 2.05, [8, 3, 5] [4, 5, 4]DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns
- 5.33, 0.47, [6, 5, 5] [4, 3, 3]Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning
- 5.33, 0.94, [6, 6, 4] [4, 3, 4]Learning to Generate Parameters from Natural Languages for Graph Neural Networks
- 5.33, 1.25, [7, 5, 4] [5, 3, 4]Adaptive Neural Trees
- 5.33, 0.47, [6, 5, 5] [2, 3, 3]Learning Internal Dense But External Sparse Structures of Deep Neural Network
- 5.33, 1.25, [5, 7, 4] [4, 3, 2]DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING
- 5.33, 1.25, [4, 5, 7] [5, 4, 4]Unseen Action Recognition with Multimodal Learning
- 5.33, 0.47, [5, 5, 6] [3, 4, 3]Equi-normalization of Neural Networks
- 5.33, 0.47, [5, 5, 6] [5, 4, 2]Adversarial Sampling for Active Learning
- 5.33, 1.70, [6, 7, 3] [5, 4, 3]CEM-RL: Combining evolutionary and gradient-based methods for policy search
- 5.33, 1.25, [7, 5, 4] [4, 3, 4]Overcoming Catastrophic Forgetting via Model Adaptation
- 5.33, 0.47, [6, 5, 5] [3, 4, 4]Hierarchically Clustered Representation Learning
- 5.33, 1.89, [8, 4, 4] [4, 4, 5]Neural Causal Discovery with Learnable Input Noise
- 5.33, 0.47, [5, 6, 5] [5, 3, 4]h-detach: Modifying the LSTM Gradient Towards Better Optimization
- 5.33, 0.94, [4, 6, 6] [3, 4, 4]Structured Neural Summarization
- 5.33, 0.94, [4, 6, 6] [3, 4, 4]Soft Q-Learning with Mutual-Information Regularization
- 5.33, 0.47, [5, 6, 5] [5, 3, 4]Set Transformer
- 5.33, 0.47, [5, 5, 6] [3, 4, 4]Learning data-derived privacy preserving representations from information metrics
- 5.33, 0.47, [6, 5, 5] [4, 4, 2]EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
- 5.33, 0.47, [5, 6, 5] [3, 2, 3]Negotiating Team Formation Using Deep Reinforcement Learning
- 5.33, 1.25, [4, 7, 5] [4, 3, 3]Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures
- 5.33, 0.47, [5, 5, 6] [4, 4, 4]Lorentzian Distance Learning
- 5.33, 1.70, [6, 7, 3] [2, 4, 2]Cohen Welling bases & SO(2)-Equivariant classifiers using Tensor nonlinearity.
- 5.33, 0.47, [5, 5, 6] [4, 5, 4]EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models
- 5.33, 0.47, [5, 6, 5] [5, 4, 4]Exploring Curvature Noise in Large-Batch Stochastic Optimization
- 5.33, 0.94, [4, 6, 6] [4, 4, 4]Transformer-XL: Language Modeling with Longer-Term Dependency
- 5.33, 0.47, [5, 5, 6] [3, 3, 3]The Case for Full-Matrix Adaptive Regularization
- 5.33, 0.94, [6, 6, 4] [4, 5, 5]BLISS in Non-Isometric Embedding Spaces
- 5.33, 1.70, [6, 3, 7] [4, 2, 4]Learning-Based Frequency Estimation Algorithms
- 5.33, 0.94, [4, 6, 6] [4, 3, 4]Hint-based Training for Non-Autoregressive Translation
- 5.33, 2.62, [9, 4, 3] [4, 4, 5]An adaptive homeostatic algorithm for the unsupervised learning of visual features
- 5.33, 1.89, [4, 8, 4] [3, 4, 4]A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks
- 5.33, 0.94, [6, 6, 4] [3, 4, 4]Knowledge Distillation from Few Samples
- 5.33, 0.47, [6, 5, 5] [4, 4, 2]Measuring and regularizing networks in function space
- 5.33, 0.47, [5, 6, 5] [4, 3, 4]Graph Classification with Geometric Scattering
- 5.33, 0.47, [5, 5, 6] [3, 3, 2]Selective Convolutional Units: Improving CNNs via Channel Selectivity
- 5.33, 0.47, [5, 5, 6] [4, 3, 5]Learning to Decompose Compound Questions with Reinforcement Learning
- 5.33, 0.47, [5, 6, 5] [4, 2, 4]Infinitely Deep Infinite-Width Networks
- 5.33, 0.47, [5, 5, 6] [4, 3, 4]State-Denoised Recurrent Neural Networks
- 5.33, 0.47, [5, 6, 5] [4, 4, 4]Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
- 5.33, 0.47, [5, 6, 5] [4, 5, 4]CDeepEx: Contrastive Deep Explanations
- 5.33, 1.25, [4, 5, 7] [3, 3, 5]Verification of Non-Linear Specifications for Neural Networks
- 5.33, 1.25, [7, 5, 4] [4, 4, 5]LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS
- 5.33, 0.47, [5, 6, 5] [4, 3, 3]Learning State Representations in Complex Systems with Multimodal Data
- 5.33, 1.70, [3, 7, 6] [3, 3, 3]Transfer and Exploration via the Information Bottleneck
- 5.33, 0.47, [6, 5, 5] [4, 3, 3]Unsupervised Conditional Generation using noise engineered mode matching GAN
- 5.33, 0.94, [6, 4, 6] [4, 3, 3]Learning to Describe Scenes with Programs
- 5.33, 2.05, [8, 5, 3] [4, 3, 4]Human-level Protein Localization with Convolutional Neural Networks
- 5.33, 1.70, [3, 7, 6] [5, 5, 3]Improved Language Modeling by Decoding the Past
- 5.33, 0.47, [5, 5, 6] [3, 3, 4]Amortized Bayesian Meta-Learning
- 5.33, 1.25, [7, 4, 5] [4, 5, 4]Coverage and Quality Driven Training of Generative Image Models
- 5.33, 0.47, [5, 5, 6] [3, 5, 3]Learning space time dynamics with PDE guided neural networks
- 5.33, 0.94, [6, 4, 6] [3, 3, 3]NLProlog: Reasoning with Weak Unification for Natural Language Question Answering
- 5.33, 0.94, [4, 6, 6] [4, 3, 3]Actor-Attention-Critic for Multi-Agent Reinforcement Learning
- 5.33, 1.25, [4, 5, 7] [4, 3, 4]Deep learning generalizes because the parameter-function map is biased towards simple functions
- 5.33, 1.25, [7, 4, 5] [4, 3, 4]Learning protein sequence embeddings using information from structure
- 5.33, 0.47, [5, 6, 5] [4, 3, 3]Meta Learning with Fast/Slow Learners
- 5.33, 1.70, [3, 6, 7] [4, 4, 4]Meta-Learning for Contextual Bandit Exploration
- 5.33, 1.25, [4, 7, 5] [3, 4, 5]Understanding & Generalizing AlphaGo Zero
- 5.33, 1.25, [4, 7, 5] [3, 4, 4]Random mesh projectors for inverse problems
- 5.33, 1.89, [8, 4, 4] [4, 4, 5]Deep Anomaly Detection with Outlier Exposure
- 5.33, 0.47, [5, 6, 5] [4, 4, 4]Probabilistic Model-Based Dynamic Architecture Search
- 5.33, 0.47, [6, 5, 5] [4, 2, 3]Mimicking actions is a good strategy for beginners: Fast Reinforcement Learning with Expert Action Sequences
- 5.33, 1.25, [5, 7, 4] [5, 5, 5]Universal Successor Features for Transfer Reinforcement Learning
- 5.33, 1.25, [7, 5, 4] [4, 4, 4]Combining Neural Networks with Personalized PageRank for Classification on Graphs
- 5.33, 1.25, [5, 4, 7] [4, 5, 4]AIM: Adversarial Inference by Matching Priors and Conditionals
- 5.33, 1.25, [4, 7, 5] [3, 4, 4]DON’T JUDGE A BOOK BY ITS COVER – ON THE DYNAMICS OF RECURRENT NEURAL NETWORKS
- 5.33, 1.25, [4, 7, 5] [3, 4, 4]The Nonlinearity Coefficient – Predicting Generalization in Deep Neural Networks
- 5.33, 1.25, [7, 4, 5] [3, 4, 4]Multi-task Learning with Gradient Communication
- 5.33, 1.25, [5, 4, 7] [3, 3, 4]Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization
- 5.33, 1.25, [4, 7, 5] [4, 4, 4]DHER: Hindsight Experience Replay for Dynamic Goals
- 5.33, 1.25, [5, 7, 4] [3, 4, 4]I Know the Feeling: Learning to Converse with Empathy
- 5.33, 0.47, [6, 5, 5] [3, 4, 4]Towards Decomposed Linguistic Representation with Holographic Reduced Representation
- 5.33, 2.05, [5, 3, 8] [4, 5, 4]Heated-Up Softmax Embedding
- 5.33, 1.89, [8, 4, 4] [2, 4, 4]Advocacy Learning
- 5.33, 1.25, [4, 7, 5] [4, 4, 3]A Modern Take on the Bias-Variance Tradeoff in Neural Networks
- 5.33, 0.47, [6, 5, 5] [2, 4, 3]Surprising Negative Results for Generative Adversarial Tree Search
- 5.33, 0.47, [5, 5, 6] [5, 3, 5]Exploring the interpretability of LSTM neural networks over multi-variable data
- 5.33, 0.94, [6, 6, 4] [4, 4, 3]Probabilistic Federated Neural Matching
- 5.33, 0.47, [5, 5, 6] [3, 3, 4]Importance Resampling for Off-policy Policy Evaluation
- 5.33, 0.47, [6, 5, 5] [1, 3, 5]Deep Imitative Models for Flexible Inference, Planning, and Control
- 5.33, 0.47, [6, 5, 5] [4, 4, 3]Complementary-label learning for arbitrary losses and models
- 5.33, 0.47, [5, 5, 6] [4, 4, 4]Online Hyperparameter Adaptation via Amortized Proximal Optimization
- 5.33, 0.47, [6, 5, 5] [4, 4, 2]DEEP GRAPH TRANSLATION
- 5.33, 0.94, [6, 6, 4] [3, 4, 5]Adapting Auxiliary Losses Using Gradient Similarity
- 5.33, 3.09, [7, 8, 1] [4, 4, 3]Optimal Control Via Neural Networks: A Convex Approach
- 5.33, 1.25, [5, 7, 4] [4, 3, 3]Composing Entropic Policies using Divergence Correction
- 5.33, 1.25, [5, 7, 4] [3, 4, 3]Neural Predictive Belief Representations
- 5.33, 0.47, [5, 5, 6] [3, 4, 4]Learning Backpropagation-Free Deep Architectures with Kernels
- 5.33, 0.94, [4, 6, 6] [4, 4, 4]Can I trust you more? Model-Agnostic Hierarchical Explanations
- 5.33, 0.47, [5, 6, 5] [3, 4, 5]Open Loop Hyperparameter Optimization and Determinantal Point Processes
- 5.33, 1.25, [4, 5, 7] [4, 4, 3]Sorting out Lipschitz function approximation
- 5.33, 0.47, [5, 5, 6] [5, 3, 4]Knows When it Doesn’t Know: Deep Abstaining Classifiers
- 5.33, 0.47, [5, 6, 5] [3, 3, 2]Probabilistic Knowledge Graph Embeddings
- 5.33, 0.47, [5, 6, 5] [4, 3, 3]An Active Learning Framework for Efficient Robust Policy Search
- 5.33, 0.47, [5, 5, 6] [4, 2, 2]Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
- 5.33, 0.47, [6, 5, 5] [2, 4, 3]Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis
- 5.33, 0.47, [5, 6, 5] [5, 3, 4]Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
- 5.33, 0.94, [4, 6, 6] [4, 3, 4]Meta-Learning Language-Guided Policy Learning
- 5.33, 0.47, [5, 6, 5] [5, 3, 4]Neural Model-Based Reinforcement Learning for Recommendation
- 5.33, 0.47, [5, 6, 5] [3, 3, 3]MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy
- 5.33, 0.94, [4, 6, 6] [4, 3, 4]IB-GAN: Disentangled Representation Learning with Information Bottleneck GAN
- 5.33, 0.47, [5, 5, 6] [4, 2, 5]AntMan: Sparse Low-Rank Compression To Accelerate RNN Inference
- 5.33, 0.94, [4, 6, 6] [4, 2, 4]Multi-Agent Dual Learning
- 5.33, 1.25, [4, 7, 5] [4, 4, 4]Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks
- 5.33, 0.47, [6, 5, 5] [3, 4, 3]Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
- 5.33, 0.94, [4, 6, 6] [5, 3, 3]Simple Black-box Adversarial Attacks
- 5.33, 0.47, [5, 5, 6] [4, 4, 4]Interpolation-Prediction Networks for Irregularly Sampled Time Series
- 5.33, 1.25, [4, 7, 5] [4, 5, 4]SynonymNet: Multi-context Bilateral Matching for Entity Synonyms
- 5.33, 1.25, [4, 5, 7] [2, 5, 3]Synthetic Datasets for Neural Program Synthesis
- 5.33, 0.94, [4, 6, 6] [4, 3, 3]Generative Adversarial Networks for Extreme Learned Image Compression
- 5.33, 0.47, [5, 6, 5] [4, 4, 5]Local Binary Pattern Networks for Character Recognition
- 5.25, 1.30, [7, 4, 6, 4] [2, 4, 3, 4]Unified recurrent network for many feature types
- 5.25, 0.43, [5, 5, 6, 5] [5, 5, 5, 4]Sample Efficient Imitation Learning for Continuous Control
- 5.25, 1.09, [4, 7, 5, 5] [5, 3, 4, 3]Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations
- 5.25, 0.83, [6, 5, 6, 4] [3, 3, 3, 4]Generative Feature Matching Networks
- 5.25, 0.83, [6, 5, 6, 4] [4, 4, 4, 3]Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective
- 5.25, 0.83, [4, 5, 6, 6] [4, 4, 2, 4]Optimistic Acceleration for Optimization
- 5.25, 1.09, [5, 5, 7, 4] [5, 3, 4, 4]P^2IR: Universal Deep Node Representation via Partial Permutation Invariant Set Functions
- 5.00, 0.82, [6, 4, 5] [3, 4, 4]Towards Language Agnostic Universal Representations
- 5.00, 1.63, [3, 5, 7] [3, 4, 3]Transfer Learning for Estimating Causal Effects Using Neural Networks
- 5.00, 1.63, [7, 3, 5] [4, 5, 4]Reduced-Gate Convolutional LSTM Design Using Predictive Coding for Next-Frame Video Prediction
- 5.00, 1.41, [7, 4, 4] [3, 4, 4]Metric-Optimized Example Weights
- 5.00, 0.00, [5, 5, 5] [4, 4, 3]Quantization for Rapid Deployment of Deep Neural Networks
- 5.00, 0.00, [5, 5, 5] [4, 3, 4]Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
- 5.00, 0.00, [5, 5, 5] [3, 4, 4]Convergence Properties of Deep Neural Networks on Separable Data
- 5.00, 1.41, [4, 4, 7] [4, 3, 4]k-Nearest Neighbors by Means of Sequence to Sequence Deep Neural Networks and Memory Networks
- 5.00, 1.41, [4, 4, 7] [4, 3, 4]Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning
- 5.00, 0.82, [5, 4, 6] [5, 4, 4]Collapse of deep and narrow neural nets
- 5.00, 0.82, [6, 5, 4] [4, 4, 2]Déjà Vu: An Empirical Evaluation of the Memorization Properties of Convnets
- 5.00, 1.41, [7, 4, 4] [4, 5, 3]Adversarial Reprogramming of Neural Networks
- 5.00, 0.82, [6, 4, 5] [4, 4, 4]Spread Divergences
- 5.00, 0.82, [5, 4, 6] [4, 4, 4]Massively Parallel Hyperparameter Tuning
- 5.00, 0.82, [4, 5, 6] [4, 3, 3]Using Ontologies To Improve Performance In Massively Multi-label Prediction
- 5.00, 0.82, [4, 6, 5] [4, 5, 4]FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE
- 5.00, 0.82, [6, 4, 5] [4, 3, 3]Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks
- 5.00, 0.00, [5, 5, 5] [4, 5, 4]GRAPH TRANSFORMATION POLICY NETWORK FOR CHEMICAL REACTION PREDICTION
- 5.00, 1.41, [7, 4, 4] [4, 4, 3]Discrete flow posteriors for variational inference in discrete dynamical systems
- 5.00, 0.82, [4, 6, 5] [3, 3, 4]Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles
- 5.00, 0.00, [5, 5, 5] [4, 4, 3]Improving Gaussian mixture latent variable model convergence with Optimal Transport
- 5.00, 0.00, [5, 5, 5] [3, 5, 2]Volumetric Convolution: Automatic Representation Learning in Unit Ball
- 5.00, 0.82, [4, 5, 6] [3, 3, 3]Directional Analysis of Stochastic Gradient Descent via von Mises-Fisher Distributions in Deep Learning
- 5.00, 0.82, [6, 5, 4] [3, 4, 3]Convolutional Neural Networks combined with Runge-Kutta Methods
- 5.00, 0.82, [4, 5, 6] [3, 4, 4]Cumulative Saliency based Globally Balanced Filter Pruning For Efficient Convolutional Neural Networks
- 5.00, 2.16, [4, 8, 3] [5, 5, 3]Initialized Equilibrium Propagation for Backprop-Free Training
- 5.00, 2.16, [2, 6, 7] [5, 4, 4]Variational Smoothing in Recurrent Neural Network Language Models
- 5.00, 0.82, [4, 6, 5] [4, 5, 3]An Automatic Operation Batching Strategy for the Backward Propagation of Neural Networks Having Dynamic Computation Graphs
- 5.00, 0.82, [5, 6, 4] [4, 2, 3]Low Latency Privacy Preserving Inference
- 5.00, 0.82, [4, 6, 5] [4, 3, 5]Optimal margin Distribution Network
- 5.00, 0.82, [5, 6, 4] [4, 3, 5]Pyramid Recurrent Neural Networks for Multi-Scale Change-Point Detection
- 5.00, 0.00, [5, 5, 5] [4, 4, 5]Learning Discriminators as Energy Networks in Adversarial Learning
- 5.00, 0.00, [5, 5, 5] [4, 4, 4]S3TA: A Soft, Spatial, Sequential, Top-Down Attention Model
- 5.00, 0.00, [5, 5, 5] [4, 4, 3]RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
- 5.00, 1.41, [4, 4, 7] [3, 4, 4]Accelerated Value Iteration via Anderson Mixing
- 5.00, 0.82, [6, 5, 4] [4, 4, 4]On the Relationship between Neural Machine Translation and Word Alignment
- 5.00, 0.82, [5, 6, 4] [4, 4, 4]Denoise while Aggregating: Collaborative Learning in Open-Domain Question Answering
- 5.00, 0.82, [6, 5, 4] [4, 4, 5]Unicorn: Continual learning with a universal, off-policy agent
- 5.00, 0.82, [5, 6, 4] [4, 3, 5]SnapQuant: A Probabilistic and Nested Parameterization for Binary Networks
- 5.00, 0.82, [6, 4, 5] [3, 3, 3]Spatial-Winograd Pruning Enabling Sparse Winograd Convolution
- 5.00, 1.63, [7, 3, 5] [4, 4, 4]On Accurate Evaluation of GANs for Language Generation
- 5.00, 1.41, [4, 7, 4] [5, 2, 3]Cautious Deep Learning
- 5.00, 1.41, [7, 4, 4] [5, 3, 5]A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation
- 5.00, 0.82, [4, 6, 5] [4, 3, 4]Likelihood-based Permutation Invariant Loss Function for Probability Distributions
- 5.00, 0.82, [5, 4, 6] [4, 4, 3]The Effectiveness of Pre-Trained Code Embeddings
- 5.00, 0.82, [4, 5, 6] [4, 4, 3]Unsupervised Document Representation using Partition Word-Vectors Averaging
- 5.00, 0.00, [5, 5, 5] [4, 3, 4]Ada-Boundary: Accelerating the DNN Training via Adaptive Boundary Batch Selection
- 5.00, 0.82, [6, 4, 5] [4, 4, 4]Interactive Parallel Exploration for Reinforcement Learning in Continuous Action Spaces
- 5.00, 0.00, [5, 5, 5] [4, 3, 3]Revisiting Reweighted Wake-Sleep
- 5.00, 0.82, [4, 5, 6] [4, 4, 4]Teacher Guided Architecture Search
- 5.00, 0.82, [6, 4, 5] [4, 3, 4]What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning
- 5.00, 0.82, [5, 6, 4] [4, 3, 5]Connecting the Dots Between MLE and RL for Sequence Generation
- 5.00, 0.82, [5, 4, 6] [4, 4, 2]Consistent Jumpy Predictions for Videos and Scenes
- 5.00, 0.00, [5, 5, 5] [5, 5, 4]Phrase-Based Attentions
- 5.00, 0.82, [5, 6, 4] [4, 3, 5]On-Policy Trust Region Policy Optimisation with Replay Buffers
- 5.00, 0.00, [5, 5, 5] [3, 4, 3]Capacity of Deep Neural Networks under Parameter Quantization
- 5.00, 1.41, [4, 4, 7] [4, 4, 3]Probabilistic Semantic Embedding
- 5.00, 1.41, [4, 4, 7] [4, 3, 3]The Importance of Norm Regularization in Linear Graph Embedding: Theoretical Analysis and Empirical Demonstration
- 5.00, 0.00, [5, 5, 5] [3, 4, 3]Weakly-supervised Knowledge Graph Alignment with Adversarial Learning
- 5.00, 0.82, [6, 5, 4] [4, 4, 3]Point Cloud GAN
- 5.00, 0.82, [5, 4, 6] [4, 4, 5]Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
- 5.00, 0.00, [5, 5, 5] [4, 4, 4]Dataset Distillation
- 5.00, 0.82, [6, 4, 5] [2, 4, 4]Representation-Constrained Autoencoders and an Application to Wireless Positioning
- 5.00, 0.82, [5, 6, 4] [4, 3, 5]The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Minima and Regularization Effects
- 5.00, 0.82, [6, 4, 5] [4, 5, 5]A Case for Object Compositionality in Deep Generative Models of Images
- 5.00, 0.00, [5, 5, 5] [5, 4, 3]An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
- 5.00, 0.82, [5, 4, 6] [4, 4, 3]COLLABORATIVE MULTIAGENT REINFORCEMENT LEARNING IN HOMOGENEOUS SWARMS
- 5.00, 0.00, [5, 5, 5] [2, 4, 4]Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation
- 5.00, 0.00, [5, 5, 5] [4, 3, 3]Transferrable End-to-End Learning for Protein Interface Prediction
- 5.00, 1.41, [3, 6, 6] [3, 3, 1]Improved robustness to adversarial examples using Lipschitz regularization of the loss
- 5.00, 0.00, [5, 5, 5] [5, 4, 3]Dense Morphological Network: An Universal Function Approximator
- 5.00, 0.00, [5, 5, 5] [5, 2, 5]High Resolution and Fast Face Completion via Progressively Attentive GANs
- 5.00, 0.00, [5, 5, 5] [1, 3, 3]Model Comparison for Semantic Grouping
- 5.00, 0.82, [4, 6, 5] [4, 3, 4]Learning to Refer to 3D Objects with Natural Language
- 5.00, 0.82, [4, 5, 6] [4, 3, 4]Dissecting an Adversarial framework for Information Retrieval
- 5.00, 0.82, [4, 6, 5] [4, 3, 5]NETWORK COMPRESSION USING CORRELATION ANALYSIS OF LAYER RESPONSES
- 5.00, 1.41, [7, 4, 4] [4, 4, 5]On Learning Heteroscedastic Noise Models within Differentiable Bayes Filters
- 5.00, 0.82, [4, 5, 6] [4, 4, 5]Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models
- 5.00, 0.00, [5, 5, 5] [5, 4, 4]A PRIVACY-PRESERVING IMAGE CLASSIFICATION FRAMEWORK WITH A LEARNABLE OBFUSCATOR
- 5.00, 0.00, [5, 5, 5] [4, 4, 4]Learning with Random Learning Rates.
- 5.00, 0.82, [4, 6, 5] [5, 4, 5]Canonical Correlation Analysis with Implicit Distributions
- 5.00, 1.63, [3, 5, 7] [3, 4, 5]Guided Exploration in Deep Reinforcement Learning
- 5.00, 1.41, [7, 4, 4] [4, 2, 3]The GAN Landscape: Losses, Architectures, Regularization, and Normalization
- 5.00, 1.63, [5, 3, 7] [3, 4, 3]TherML: The Thermodynamics of Machine Learning
- 5.00, 0.82, [4, 5, 6] [4, 5, 3]Graph2Seq: Scalable Learning Dynamics for Graphs
- 5.00, 0.82, [5, 6, 4] [5, 4, 4]ChoiceNet: Robust Learning by Revealing Output Correlations
- 5.00, 1.41, [7, 4, 4] [5, 4, 4]N-Ary Quantization for CNN Model Compression and Inference Acceleration
- 5.00, 0.82, [5, 6, 4] [2, 4, 2]Automata Guided Skill Composition
- 5.00, 0.82, [5, 6, 4] [5, 2, 5]Learning To Plan
- 5.00, 2.16, [6, 7, 2] [3, 4, 3]Implicit Autoencoders
- 5.00, 0.82, [4, 6, 5] [5, 4, 4]COCO-GAN: Conditional Coordinate Generative Adversarial Network
- 5.00, 0.82, [6, 4, 5] [5, 2, 4]Bayesian Deep Learning via Stochastic Gradient MCMC with a Stochastic Approximation Adaptation
- 5.00, 1.41, [7, 4, 4] [3, 4, 3]Generative Ensembles for Robust Anomaly Detection
- 5.00, 0.00, [5, 5, 5] [3, 3, 5]Characterizing Malicious Edges targeting on Graph Neural Networks
- 5.00, 0.82, [5, 6, 4] [4, 3, 5]Zero-shot Dual Machine Translation
- 5.00, 0.00, [5, 5, 5] [4, 3, 4]Inferring Reward Functions from Demonstrators with Unknown Biases
- 5.00, 0.00, [5, 5, 5] [3, 4, 4]A comprehensive, application-oriented study of catastrophic forgetting in DNNs
- 5.00, 0.82, [5, 6, 4] [4, 4, 5]Deep Reinforcement Learning of Universal Policies with Diverse Environment Summaries
- 5.00, 2.16, [2, 7, 6] [4, 3, 3]RANDOM MASK: Towards Robust Convolutional Neural Networks
- 5.00, 0.00, [5, 5, 5] [4, 5, 5]Bias Also Matters: Bias Attribution for Deep Neural Network Explanation
- 5.00, 0.82, [6, 4, 5] [4, 4, 2]Label Propagation Networks
- 5.00, 1.63, [5, 3, 7] [3, 4, 2]Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations
- 5.00, 0.82, [6, 4, 5] [4, 5, 5]Learning Global Additive Explanations for Neural Nets Using Model Distillation
- 5.00, 0.82, [6, 5, 4] [3, 3, 4]Understand the dynamics of GANs via Primal-Dual Optimization
- 5.00, 0.82, [6, 4, 5] [4, 4, 4]Rethinking learning rate schedules for stochastic optimization
- 5.00, 1.41, [4, 7, 4] [4, 3, 4]Learning and Planning with a Semantic Model
- 5.00, 0.00, [5, 5, 5] [3, 4, 3]Metropolis-Hastings view on variational inference and adversarial training
- 5.00, 0.82, [6, 5, 4] [4, 5, 4]Learning To Simulate
- 5.00, 1.41, [3, 6, 6] [5, 4, 4]Graph2Seq: Graph to Sequence Learning with Attention-Based Neural Networks
- 5.00, 0.82, [4, 6, 5] [3, 3, 4]Information Regularized Neural Networks
- 5.00, 0.82, [5, 4, 6] [4, 4, 3]Transfer Learning for Sequences via Learning to Collocate
- 5.00, 0.82, [6, 4, 5] [4, 3, 3]Guided Evolutionary Strategies: Escaping the curse of dimensionality in random search
- 5.00, 0.82, [5, 6, 4] [5, 4, 3]Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
- 5.00, 0.82, [4, 6, 5] [4, 4, 4]Learning Actionable Representations with Goal Conditioned Policies
- 5.00, 1.41, [7, 4, 4] [3, 4, 2]Shrinkage-based Bias-Variance Trade-off for Deep Reinforcement Learning
- 5.00, 1.41, [4, 4, 7] [4, 4, 4]A RECURRENT NEURAL CASCADE-BASED MODEL FOR CONTINUOUS-TIME DIFFUSION PROCESS
- 5.00, 0.00, [5, 5, 5] [4, 4, 4]ON THE EFFECTIVENESS OF TASK GRANULARITY FOR TRANSFER LEARNING
- 5.00, 1.41, [4, 4, 7] [5, 3, 3]NATTACK: A STRONG AND UNIVERSAL GAUSSIAN BLACK-BOX ADVERSARIAL ATTACK
- 5.00, 0.82, [5, 6, 4] [4, 4, 5]Dynamic Graph Representation Learning via Self-Attention Networks
- 5.00, 0.00, [5, 5, 5] [4, 4, 3]Inducing Cooperation via Learning to reshape rewards in semi-cooperative multi-agent reinforcement learning
- 5.00, 0.00, [5, 5, 5] [5, 5, 4]VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Short Learning
- 5.00, 1.63, [3, 5, 7] [4, 3, 2]Noisy Information Bottlenecks for Generalization
- 5.00, 0.00, [5, 5, 5] [4, 5, 4]Learning Diverse Generations using Determinantal Point Processes
- 5.00, 0.00, [5, 5, 5] [4, 3, 4]Learning Representations of Categorical Feature Combinations via Self-Attention
- 5.00, 0.82, [4, 6, 5] [4, 4, 5]MLPrune: Multi-Layer Pruning for Automated Neural Network Compression
- 5.00, 1.41, [4, 4, 7] [4, 4, 4]Zero-shot Learning for Speech Recognition with Universal Phonetic Model
- 5.00, 0.82, [4, 5, 6] [4, 5, 2]Reinforced Imitation Learning from Observations
- 5.00, 0.82, [4, 5, 6] [5, 4, 2]Link Prediction in Hypergraphs using Graph Convolutional Networks
- 5.00, 0.82, [4, 6, 5] [5, 3, 4]Structured Content Preservation for Unsupervised Text Style Transfer
- 5.00, 0.00, [5, 5, 5] [2, 3, 5]Riemannian TransE: Multi-relational Graph Embedding in Non-Euclidean Space
- 5.00, 0.82, [6, 4, 5] [2, 4, 3]On Regularization and Robustness of Deep Neural Networks
- 5.00, 0.82, [6, 5, 4] [3, 3, 3]Scalable Neural Theorem Proving on Knowledge Bases and Natural Language
- 5.00, 2.16, [8, 3, 4] [5, 5, 5]Learning to remember: Dynamic Generative Memory for Continual Learning
- 5.00, 0.82, [6, 4, 5] [4, 4, 4]A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
- 5.00, 0.82, [5, 4, 6] [5, 4, 3]Human-Guided Column Networks: Augmenting Deep Learning with Advice
- 5.00, 0.82, [4, 6, 5] [5, 2, 4]Double Neural Counterfactual Regret Minimization
- 5.00, 0.82, [4, 5, 6] [3, 3, 4]Transferring SLU Models in Novel Domains
- 5.00, 2.16, [3, 4, 8] [5, 5, 4]Analysis of Memory Organization for Dynamic Neural Networks
- 5.00, 0.82, [6, 5, 4] [5, 3, 4]Systematic Generalization: What Is Required and Can It Be Learned?
- 5.00, 0.82, [5, 6, 4] [4, 4, 4]Context Mover’s Distance & Barycenters: Optimal transport of contexts for building representations
- 5.00, 1.22, [6, 5, 6, 3] [3, 3, 3, 5]Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search
- 5.00, 0.82, [4, 6, 5] [5, 4, 4]Successor Options : An Option Discovery Algorithm for Reinforcement Learning
- 5.00, 0.82, [4, 5, 6] [4, 3, 5]STCN: Stochastic Temporal Convolutional Networks
- 5.00, 0.82, [6, 4, 5] [4, 5, 4]Analyzing Federated Learning through an Adversarial Lens
- 5.00, 1.22, [7, 4, 4, 5] [4, 4, 3, 4]Causal Reasoning from Meta-learning
- 5.00, 0.82, [6, 4, 5] [4, 3, 4]AD-VAT: An Asymmetric Dueling mechanism for learning Visual Active Tracking
- 5.00, 0.00, [5, 5, 5] [4, 3, 5]Incremental Few-Shot Learning with Attention Attractor Networks
- 5.00, 0.82, [6, 5, 4] [4, 4, 3]GenEval: A Benchmark Suite for Evaluating Generative Models
- 5.00, 0.82, [4, 5, 6] [5, 5, 3]Approximation capability of neural networks on sets of probability measures and tree-structured data
- 5.00, 0.82, [4, 6, 5] [3, 3, 4]Robustness Certification with Refinement
- 5.00, 0.82, [6, 4, 5] [3, 5, 3]Intrinsic Social Motivation via Causal Influence in Multi-Agent RL
- 5.00, 0.00, [5, 5, 5] [4, 4, 4]Making Convolutional Networks Shift-Invariant Again
- 5.00, 0.82, [6, 5, 4] [4, 4, 4]Adversarial Audio Super-Resolution with Unsupervised Feature Losses
- 5.00, 1.63, [3, 7, 5] [4, 5, 4]ACTRCE: Augmenting Experience via Teacher’s Advice
- 5.00, 0.00, [5, 5, 5] [3, 4, 3]Learnable Embedding Space for Efficient Neural Architecture Compression
- 5.00, 1.41, [4, 7, 4] [5, 4, 3]ISA-VAE: Independent Subspace Analysis with Variational Autoencoders
- 5.00, 0.82, [6, 5, 4] [3, 3, 4]Interpretable Continual Learning
- 5.00, 0.00, [5, 5, 5] [5, 4, 5]Experience replay for continual learning
- 5.00, 0.82, [6, 5, 4] [4, 3, 4]Accelerated Gradient Flow for Probability Distributions
- 5.00, 0.00, [5, 5, 5] [3, 3, 3]Learning to Progressively Plan
- 5.00, 0.82, [5, 4, 6] [4, 4, 4]Capsules Graph Neural Network
- 5.00, 0.82, [5, 4, 6] [4, 4, 5]Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
- 5.00, 0.82, [5, 6, 4] [3, 4, 3]Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification
- 5.00, 0.82, [5, 6, 4] [2, 4, 4]Graph2Graph Networks for Multi-Label Classification
- 5.00, 1.41, [3, 6, 6] [4, 4, 4]Towards GAN Benchmarks Which Require Generalization
- 5.00, 0.71, [5, 6, 4, 5] [5, 3, 5, 5]TTS-GAN: a generative adversarial network for style modeling in a text-to-speech system
- 5.00, 1.22, [3, 6, 5, 6] [4, 3, 4, 3]A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs
- 5.00, 0.82, [5, 4, 6] [5, 4, 4]Local Image-to-Image Translation via Pixel-wise Highway Adaptive Instance Normalization
- 5.00, 0.82, [4, 6, 5] [4, 4, 5]INFORMATION MAXIMIZATION AUTO-ENCODING
- 5.00, 0.82, [4, 6, 5] [4, 5, 5]Generative Adversarial Self-Imitation Learning
- 5.00, 1.41, [7, 4, 4] [3, 3, 3]Generative Adversarial Models for Learning Private and Fair Representations
- 4.80, 1.17, [6, 3, 4, 6, 5] [5, 4, 4, 2, 4]Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
- 4.75, 0.83, [6, 4, 4, 5] [3, 2, 3, 3]Cutting Down Training Memory by Re-fowarding
- 4.75, 0.83, [5, 6, 4, 4] [5, 4, 4, 4]Multi-turn Dialogue Response Generation in an Adversarial Learning Framework
- 4.75, 0.43, [5, 5, 4, 5] [2, 2, 4, 5]Pooling Is Neither Necessary nor Sufficient for Appropriate Deformation Stability in CNNs
- 4.75, 1.92, [8, 3, 4, 4] [2, 5, 5, 4]Geomstats: a Python Package for Riemannian Geometry in Machine Learning
- 4.75, 1.48, [4, 3, 7, 5] [3, 4, 4, 4]Towards a better understanding of Vector Quantized Autoencoders
- 4.67, 0.47, [4, 5, 5] [2, 3, 3]Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems
- 4.67, 1.70, [7, 3, 4] [3, 5, 4]CHEMICAL NAMES STANDARDIZATION USING NEURAL SEQUENCE TO SEQUENCE MODEL
- 4.67, 0.94, [6, 4, 4] [5, 1, 4]Traditional and Heavy Tailed Self Regularization in Neural Network Models
- 4.67, 0.47, [4, 5, 5] [3, 2, 4]Count-Based Exploration with the Successor Representation
- 4.67, 0.47, [5, 5, 4] [3, 4, 4]Learning Graph Representations by Dendrograms
- 4.67, 0.47, [5, 4, 5] [2, 3, 4]Efficient Dictionary Learning with Gradient Descent
- 4.67, 1.25, [3, 6, 5] [5, 2, 5]$A^*$ sampling with probability matching
- 4.67, 0.47, [4, 5, 5] [3, 5, 3]Neural Variational Inference For Embedding Knowledge Graphs
- 4.67, 0.94, [4, 4, 6] [4, 4, 4]SupportNet: solving catastrophic forgetting in class incremental learning with support data
- 4.67, 0.94, [4, 6, 4] [4, 5, 4]Unsupervised Image to Sequence Translation with Canvas-Drawer Networks
- 4.67, 1.70, [7, 3, 4] [4, 5, 3]Unsupervised Word Discovery with Segmental Neural Language Models
- 4.67, 1.25, [6, 3, 5] [4, 5, 4]Generative Adversarial Network Training is a Continual Learning Problem
- 4.67, 1.70, [7, 4, 3] [4, 3, 4]GENERALIZED ADAPTIVE MOMENT ESTIMATION
- 4.67, 0.47, [5, 5, 4] [5, 3, 3]Effective and Efficient Batch Normalization Using Few Uncorrelated Data for Statistics’ Estimation
- 4.67, 0.94, [4, 6, 4] [4, 5, 4]TequilaGAN: How To Easily Identify GAN Samples
- 4.67, 0.94, [6, 4, 4] [4, 4, 3]Gradient Descent Happens in a Tiny Subspace
- 4.67, 1.25, [5, 6, 3] [4, 4, 4]Dual Skew Divergence Loss for Neural Machine Translation
- 4.67, 0.47, [4, 5, 5] [3, 4, 3]Stochastic Learning of Additive Second-Order Penalties with Applications to Fairness
- 4.67, 0.94, [6, 4, 4] [5, 4, 4]Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
- 4.67, 0.94, [4, 4, 6] [4, 4, 4]Boosting Trust Region Policy Optimization by Normalizing flows Policy
- 4.67, 0.47, [4, 5, 5] [4, 3, 4]Backplay: ‘Man muss immer umkehren’
- 4.67, 0.94, [4, 4, 6] [4, 4, 4]HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS
- 4.67, 0.94, [6, 4, 4] [4, 3, 4]Improved resistance of neural networks to adversarial images through generative pre-training
- 4.67, 0.47, [4, 5, 5] [4, 5, 3]Context-aware Forecasting for Multivariate Stationary Time-series
- 4.67, 0.94, [4, 6, 4] [4, 4, 5]Selective Self-Training for semi-supervised Learning
- 4.67, 0.94, [4, 4, 6] [5, 3, 4]Learning with Little Data: Evaluation of Deep Learning Algorithms
- 4.67, 1.70, [7, 3, 4] [5, 4, 4]What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation
- 4.67, 0.94, [6, 4, 4] [3, 4, 4]Improving latent variable descriptiveness by modelling rather than ad-hoc factors
- 4.67, 0.47, [5, 5, 4] [4, 3, 4]Conditional Network Embeddings
- 4.67, 1.70, [7, 3, 4] [3, 4, 3]Holographic and other Point Set Distances for Machine Learning
- 4.67, 0.94, [6, 4, 4] [3, 3, 4]Unsupervised Emergence of Spatial Structure from Sensorimotor Prediction
- 4.67, 0.47, [4, 5, 5] [5, 4, 4]PRUNING IN TRAINING: LEARNING AND RANKING SPARSE CONNECTIONS IN DEEP CONVOLUTIONAL NETWORKS
- 4.67, 0.47, [4, 5, 5] [4, 5, 4]RelWalk — A Latent Variable Model Approach to Knowledge Graph Embedding
- 4.67, 0.47, [5, 5, 4] [2, 3, 4]Unsupervised Expectation Learning for Multisensory Binding
- 4.67, 1.25, [6, 5, 3] [4, 4, 4]Sentence Encoding with Tree-Constrained Relation Networks
- 4.67, 0.47, [4, 5, 5] [3, 2, 3]Pushing the bounds of dropout
- 4.67, 0.94, [4, 6, 4] [4, 5, 4]StrokeNet: A Neural Painting Environment
- 4.67, 1.25, [5, 6, 3] [2, 2, 4]Intriguing Properties of Learned Representations
- 4.67, 1.25, [5, 3, 6] [4, 4, 4]Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
- 4.67, 0.47, [5, 5, 4] [4, 4, 4]Computation-Efficient Quantization Method for Deep Neural Networks
- 4.67, 1.25, [3, 5, 6] [4, 5, 3]Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels
- 4.67, 0.47, [5, 5, 4] [4, 3, 4]Consistency-based anomaly detection with adaptive multiple-hypotheses predictions
- 4.67, 1.25, [5, 6, 3] [2, 4, 5]Integrated Steganography and Steganalysis with Generative Adversarial Networks
- 4.67, 0.47, [4, 5, 5] [4, 4, 5]Rectified Gradient: Layer-wise Thresholding for Sharp and Coherent Attribution Maps
- 4.67, 0.94, [6, 4, 4] [5, 4, 4]Generative replay with feedback connections as a general strategy for continual learning
- 4.67, 1.25, [6, 5, 3] [2, 3, 4]Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network
- 4.67, 0.94, [6, 4, 4] [5, 3, 4]Effective Path: Know the Unknowns of Neural Network
- 4.67, 1.25, [3, 6, 5] [4, 4, 4]Siamese Capsule Networks
- 4.67, 0.47, [4, 5, 5] [5, 3, 4]Ergodic Measure Preserving Flows
- 4.67, 1.25, [5, 3, 6] [5, 5, 4]3D-RelNet: Joint Object and Relational Network for 3D Prediction
- 4.67, 0.47, [5, 5, 4] [4, 5, 4]Finding Mixed Nash Equilibria of Generative Adversarial Networks
- 4.67, 0.47, [5, 4, 5] [5, 5, 4]Investigating CNNs’ Learning Representation under label noise
- 4.67, 0.94, [4, 6, 4] [5, 4, 4]Conscious Inference for Object Detection
- 4.67, 0.47, [5, 4, 5] [4, 2, 3]Learning Information Propagation in the Dynamical Systems via Information Bottleneck Hierarchy
- 4.67, 0.47, [5, 4, 5] [2, 5, 4]TabNN: A Universal Neural Network Solution for Tabular Data
- 4.67, 1.25, [3, 5, 6] [3, 2, 4]Probabilistic Binary Neural Networks
- 4.67, 0.47, [5, 4, 5] [5, 4, 3]Gradient-based learning for F-measure and other performance metrics
- 4.67, 0.47, [4, 5, 5] [4, 5, 2]SEGEN: SAMPLE-ENSEMBLE GENETIC EVOLUTIONARY NETWORK MODEL
- 4.67, 0.94, [6, 4, 4] [4, 4, 4]Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
- 4.67, 1.25, [5, 6, 3] [4, 4, 4]Learning to Drive by Observing the Best and Synthesizing the Worst
- 4.67, 1.89, [6, 2, 6] [5, 5, 3]Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning
- 4.67, 1.25, [3, 6, 5] [3, 4, 3]MARGINALIZED AVERAGE ATTENTIONAL NETWORK FOR WEAKLY-SUPERVISED LEARNING
- 4.67, 1.70, [3, 7, 4] [3, 5, 4]Discriminative out-of-distribution detection for semantic segmentation
- 4.67, 0.47, [5, 5, 4] [4, 4, 3]Integral Pruning on Activations and Weights for Efficient Neural Networks
- 4.67, 0.47, [4, 5, 5] [4, 4, 4]Online Bellman Residue Minimization via Saddle Point Optimization
- 4.67, 0.47, [5, 4, 5] [4, 5, 4]Area Attention
- 4.67, 0.47, [5, 4, 5] [2, 3, 2]NEURAL MALWARE CONTROL WITH DEEP REINFORCEMENT LEARNING
- 4.67, 0.47, [5, 5, 4] [4, 5, 4]Variational Sparse Coding
- 4.67, 0.94, [6, 4, 4] [5, 3, 4]What Information Does a ResNet Compress?
- 4.67, 1.25, [5, 6, 3] [5, 4, 5]Interpreting Adversarial Robustness: A View from Decision Surface in Input Space
- 4.67, 0.47, [5, 5, 4] [4, 3, 4]LIT: Block-wise Intermediate Representation Training for Model Compression
- 4.67, 0.47, [5, 4, 5] [5, 4, 4]An Energy-Based Framework for Arbitrary Label Noise Correction
- 4.67, 0.94, [6, 4, 4] [1, 3, 2]ACE: Artificial Checkerboard Enhancer to Induce and Evade Adversarial Attacks
- 4.67, 0.47, [4, 5, 5] [4, 3, 4]SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
- 4.67, 0.94, [6, 4, 4] [4, 5, 4]Differentiable Expected BLEU for Text Generation
- 4.67, 0.47, [4, 5, 5] [4, 4, 4]Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching
- 4.67, 1.25, [6, 3, 5] [4, 4, 3]Exploiting Environmental Variation to Improve Policy Robustness in Reinforcement Learning
- 4.67, 0.47, [4, 5, 5] [4, 3, 4]Sufficient Conditions for Robustness to Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks
- 4.67, 0.47, [4, 5, 5] [5, 4, 3]Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
- 4.67, 0.47, [5, 5, 4] [4, 4, 3]PAIRWISE AUGMENTED GANS WITH ADVERSARIAL RECONSTRUCTION LOSS
- 4.67, 0.47, [5, 5, 4] [4, 3, 5]Learned optimizers that outperform on wall-clock and validation loss
- 4.67, 0.94, [4, 6, 4] [4, 4, 5]Stability of Stochastic Gradient Method with Momentum for Strongly Convex Loss Functions
- 4.67, 0.47, [5, 4, 5] [4, 3, 5]When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models?
- 4.67, 0.47, [5, 4, 5] [4, 5, 3]Convergence Guarantees for RMSProp and ADAM in Non-Convex Optimization and an Empirical Comparison to Nesterov Acceleration
- 4.67, 0.94, [4, 6, 4] [5, 4, 4]Geometry aware convolutional filters for omnidirectional images representation
- 4.67, 0.94, [6, 4, 4] [2, 3, 5]FEATURE PRIORITIZATION AND REGULARIZATION IMPROVE STANDARD ACCURACY AND ADVERSARIAL ROBUSTNESS
- 4.67, 0.47, [4, 5, 5] [4, 5, 3]Learning Gibbs-regularized GANs with variational discriminator reparameterization
- 4.67, 0.47, [5, 4, 5] [4, 4, 2]Neural separation of observed and unobserved distributions
- 4.67, 0.47, [5, 4, 5] [3, 3, 3]Penetrating the Fog: the Path to Efficient CNN Models
- 4.67, 0.94, [4, 4, 6] [4, 3, 4]Expressiveness in Deep Reinforcement Learning
- 4.67, 0.47, [4, 5, 5] [5, 4, 4]Generating Realistic Stock Market Order Streams
- 4.67, 0.47, [4, 5, 5] [5, 3, 4]An investigation of model-free planning
- 4.67, 1.25, [3, 6, 5] [5, 3, 3]Selectivity metrics can overestimate the selectivity of units: a case study on AlexNet
- 4.67, 0.47, [5, 5, 4] [4, 3, 4]CNNSAT: Fast, Accurate Boolean Satisfiability using Convolutional Neural Networks
- 4.67, 0.47, [5, 5, 4] [3, 5, 5]Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks
- 4.67, 0.94, [6, 4, 4] [4, 4, 4]Sliced Wasserstein Auto-Encoders
- 4.67, 1.25, [5, 3, 6] [4, 3, 5]End-to-end learning of pharmacological assays from high-resolution microscopy images
- 4.67, 1.25, [3, 5, 6] [4, 3, 4]Safe Policy Learning from Observations
- 4.67, 0.94, [4, 4, 6] [4, 4, 3]A Study of Robustness of Neural Nets Using Approximate Feature Collisions
- 4.67, 0.47, [5, 5, 4] [4, 3, 3]SSoC: Learning Spontaneous and Self-Organizing Communication for Multi-Agent Collaboration
- 4.67, 1.25, [6, 3, 5] [4, 4, 3]On the Geometry of Adversarial Examples
- 4.67, 0.47, [4, 5, 5] [4, 3, 3]Neural Networks with Structural Resistance to Adversarial Attacks
- 4.67, 0.47, [5, 4, 5] [4, 4, 4]Partially Mutual Exclusive Softmax for Positive and Unlabeled data
- 4.67, 1.25, [3, 5, 6] [4, 4, 4]Unsupervised Disentangling Structure and Appearance
- 4.67, 0.47, [4, 5, 5] [4, 4, 4]Success at any cost: value constrained model-free continuous control
- 4.67, 0.47, [5, 4, 5] [4, 4, 3]Predictive Uncertainty through Quantization
- 4.67, 0.94, [6, 4, 4] [4, 5, 5]Maximum a Posteriori on a Submanifold: a General Image Restoration Method with GAN
- 4.67, 0.47, [5, 4, 5] [4, 4, 4]Zero-training Sentence Embedding via Orthogonal Basis
- 4.67, 0.47, [5, 4, 5] [4, 4, 4]The Expressive Power of Gated Recurrent Units as a Continuous Dynamical System
- 4.67, 0.94, [4, 4, 6] [4, 5, 3]SIMILE: Introducing Sequential Information towards More Effective Imitation Learning
- 4.67, 2.05, [7, 2, 5] [4, 5, 3]Meta-learning with differentiable closed-form solvers
- 4.67, 1.25, [3, 6, 5] [4, 4, 4]Mode Normalization
- 4.67, 0.94, [4, 6, 4] [2, 4, 4]Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks
- 4.67, 1.25, [3, 5, 6] [4, 3, 4]NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search
- 4.67, 1.70, [4, 7, 3] [3, 4, 4]A theoretical framework for deep and locally connected ReLU network
- 4.67, 0.94, [4, 6, 4] [3, 3, 4]Approximation and non-parametric estimation of ResNet-type convolutional neural networks via block-sparse fully-connected neural networks
- 4.67, 0.47, [5, 5, 4] [4, 3, 5]Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
- 4.67, 1.25, [3, 6, 5] [1, 4, 4]Pix2Scene: Learning Implicit 3D Representations from Images
- 4.67, 0.94, [4, 4, 6] [2, 5, 3]A Proposed Hierarchy of Deep Learning Tasks
- 4.67, 0.47, [5, 4, 5] [5, 5, 4]CGNF: Conditional Graph Neural Fields
- 4.67, 0.94, [6, 4, 4] [4, 4, 3]Self-Supervised Generalisation with Meta Auxiliary Learning
- 4.67, 0.47, [4, 5, 5] [4, 4, 2]Theoretical and Empirical Study of Adversarial Examples
- 4.67, 1.70, [4, 3, 7] [4, 4, 3]Coupled Recurrent Models for Polyphonic Music Composition
- 4.67, 0.94, [4, 6, 4] [3, 3, 4]DEEP-TRIM: REVISITING L1 REGULARIZATION FOR CONNECTION PRUNING OF DEEP NETWORK
- 4.67, 0.47, [5, 4, 5] [2, 4, 3]Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning
- 4.67, 0.47, [5, 5, 4] [4, 4, 4]Model Compression with Generative Adversarial Networks
- 4.67, 1.25, [6, 5, 3] [4, 4, 4]Text Infilling
- 4.67, 1.25, [6, 3, 5] [4, 3, 4]Visual Imitation with a Minimal Adversary
- 4.67, 1.25, [6, 3, 5] [3, 3, 3]Novel positional encodings to enable tree-structured transformers
- 4.67, 0.47, [5, 4, 5] [4, 4, 4]Shaping representations through communication
- 4.67, 0.47, [5, 4, 5] [3, 3, 4]Characterizing Vulnerabilities of Deep Reinforcement Learning
- 4.67, 0.47, [4, 5, 5] [4, 3, 4]Multi-Grained Entity Proposal Network for Named Entity Recognition
- 4.67, 0.47, [5, 5, 4] [3, 4, 4]Measuring Density and Similarity of Task Relevant Information in Neural Representations
- 4.67, 0.47, [5, 5, 4] [4, 3, 4]Outlier Detection from Image Data
- 4.67, 0.47, [5, 5, 4] [4, 3, 5]Accelerated Sparse Recovery Under Structured Measurements
- 4.67, 0.94, [6, 4, 4] [3, 3, 4]Object-Oriented Model Learning through Multi-Level Abstraction
- 4.67, 1.70, [3, 7, 4] [4, 3, 3]Learning to control self-assembling morphologies: a study of generalization via modularity
- 4.67, 0.47, [5, 5, 4] [4, 4, 4]Using GANs for Generation of Realistic City-Scale Ride Sharing/Hailing Data Sets
- 4.67, 0.47, [4, 5, 5] [3, 4, 4]Manifold Alignment via Feature Correspondence
- 4.67, 1.70, [3, 4, 7] [4, 4, 3]Explicit Recall for Efficient Exploration
- 4.67, 0.47, [5, 4, 5] [4, 4, 3]Differential Equation Networks
- 4.67, 0.47, [5, 4, 5] [4, 4, 4]Predicting the Present and Future States of Multi-agent Systems from Partially-observed Visual Data
- 4.67, 0.47, [5, 5, 4] [4, 4, 5]Learning shared manifold representation of images and attributes for generalized zero-shot learning
- 4.67, 0.47, [5, 4, 5] [3, 5, 4]Inference of unobserved event streams with neural Hawkes particle smoothing
- 4.50, 0.50, [5, 4] [3, 3]Improving On-policy Learning with Statistical Reward Accumulation
- 4.50, 0.50, [5, 4, 4, 5] [2, 3, 2, 2]Unification of Recurrent Neural Network Architectures and Quantum Inspired Stable Design
- 4.50, 0.50, [5, 5, 4, 4] [3, 3, 4, 4]One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
- 4.50, 0.50, [4, 5] [4, 4]Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
- 4.50, 0.50, [5, 4, 4, 5] [3, 4, 4, 4]Music Transformer
- 4.40, 0.80, [6, 4, 4, 4, 4] [4, 5, 4, 3, 5]Context Dependent Modulation of Activation Function
- 4.33, 0.47, [5, 4, 4] [4, 4, 2]Unsupervised classification into unknown k classes
- 4.33, 0.47, [4, 4, 5] [4, 5, 4]Adaptive Convolutional ReLUs
- 4.33, 0.47, [4, 4, 5] [4, 3, 3]FEED: Feature-level Ensemble Effect for knowledge Distillation
- 4.33, 1.89, [3, 3, 7] [4, 3, 3]Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks
- 4.33, 1.25, [4, 6, 3] [3, 2, 4]Variation Network: Learning High-level Attributes for Controlled Input Manipulation
- 4.33, 0.94, [5, 3, 5] [3, 5, 4]Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference
- 4.33, 1.25, [3, 6, 4] [4, 4, 3]Targeted Adversarial Examples for Black Box Audio Systems
- 4.33, 0.47, [4, 4, 5] [4, 4, 4]Neuron Hierarchical Networks
- 4.33, 1.70, [6, 5, 2] [5, 4, 5]Online Learning for Supervised Dimension Reduction
- 4.33, 0.47, [4, 5, 4] [4, 4, 4]Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
- 4.33, 0.47, [4, 4, 5] [4, 4, 3]MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA
- 4.33, 1.25, [4, 6, 3] [2, 3, 4]Unsupervised Meta-Learning for Reinforcement Learning
- 4.33, 1.70, [5, 2, 6] [3, 5, 3]q-Neurons: Neuron Activations based on Stochastic Jackson’s Derivative Operators
- 4.33, 0.47, [5, 4, 4] [4, 4, 4]Learning a Neural-network-based Representation for Open Set Recognition
- 4.33, 0.47, [4, 4, 5] [4, 4, 4]No Pressure! Addressing Problem of Local Minima in Manifold Learning
- 4.33, 1.25, [3, 4, 6] [5, 3, 3]On the Convergence and Robustness of Batch Normalization
- 4.33, 0.47, [4, 5, 4] [4, 5, 4]Sample Efficient Deep Neuroevolution in Low Dimensional Latent Space
- 4.33, 1.25, [6, 3, 4] [3, 5, 4]Context-adaptive Entropy Model for End-to-end Optimized Image Compression
- 4.33, 1.25, [4, 3, 6] [4, 4, 3]An Adversarial Learning Framework for a Persona-based Multi-turn Dialogue Model
- 4.33, 0.47, [4, 4, 5] [4, 4, 4]ODIN: Outlier Detection In Neural Networks
- 4.33, 0.47, [5, 4, 4] [4, 4, 4]Log Hyperbolic Cosine Loss Improves Variational Auto-Encoder
- 4.33, 0.94, [5, 3, 5] [4, 4, 4]Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
- 4.33, 0.47, [5, 4, 4] [3, 5, 3]A preconditioned accelerated stochastic gradient descent algorithm
- 4.33, 0.47, [4, 4, 5] [4, 3, 3]Local Stability and Performance of Simple Gradient Penalty $\mu$-Wasserstein GAN
- 4.33, 0.47, [5, 4, 4] [3, 4, 4]Efficient Convolutional Neural Network Training with Direct Feedback Alignment
- 4.33, 1.25, [3, 4, 6] [5, 5, 2]LEARNING ADVERSARIAL EXAMPLES WITH RIEMANNIAN GEOMETRY
- 4.33, 0.47, [4, 5, 4] [5, 3, 5]SHAMANN: Shared Memory Augmented Neural Networks
- 4.33, 0.47, [4, 4, 5] [4, 3, 3]Adaptive Convolutional Neural Networks
- 4.33, 1.25, [3, 6, 4] [4, 3, 3]Pixel Redrawn For A Robust Adversarial Defense
- 4.33, 0.47, [4, 4, 5] [4, 3, 3]DeepTwist: Learning Model Compression via Occasional Weight Distortion
- 4.33, 1.25, [4, 6, 3] [3, 3, 5]Wasserstein proximal of GANs
- 4.33, 1.25, [4, 3, 6] [4, 4, 2]Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
- 4.33, 0.94, [3, 5, 5] [5, 2, 3]Exploration by Uncertainty in Reward Space
- 4.33, 0.47, [4, 5, 4] [4, 4, 5]Contextualized Role Interaction for Neural Machine Translation
- 4.33, 0.47, [4, 4, 5] [4, 4, 3]Escaping Flat Areas via Function-Preserving Structural Network Modifications
- 4.33, 0.47, [4, 5, 4] [4, 3, 4]DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search
- 4.33, 0.47, [4, 5, 4] [4, 4, 3]Classifier-agnostic saliency map extraction
- 4.33, 0.47, [4, 5, 4] [4, 4, 3]PRUNING WITH HINTS: AN EFFICIENT FRAMEWORK FOR MODEL ACCELERATION
- 4.33, 0.94, [3, 5, 5] [3, 4, 4]Meta-Learning with Individualized Feature Space for Few-Shot Classification
- 4.33, 0.94, [5, 5, 3] [2, 2, 3]Downsampling leads to Image Memorization in Convolutional Autoencoders
- 4.33, 1.25, [3, 6, 4] [4, 5, 3]FAST OBJECT LOCALIZATION VIA SENSITIVITY ANALYSIS
- 4.33, 0.47, [4, 5, 4] [4, 3, 4]Generative Models from the perspective of Continual Learning
- 4.33, 0.47, [4, 5, 4] [3, 5, 5]Total Style Transfer with a Single Feed-Forward Network
- 4.33, 0.94, [3, 5, 5] [4, 5, 5]A fast quasi-Newton-type method for large-scale stochastic optimisation
- 4.33, 0.94, [5, 5, 3] [4, 4, 4]Explainable Adversarial Learning: Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness
- 4.33, 0.47, [5, 4, 4] [4, 5, 5]Universal Attacks on Equivariant Networks
- 4.33, 0.94, [5, 5, 3] [4, 4, 4]Compound Density Networks
- 4.33, 0.47, [4, 5, 4] [5, 2, 3]A Guider Network for Multi-Dual Learning
- 4.33, 0.94, [5, 5, 3] [3, 4, 4]ON BREIMAN’S DILEMMA IN NEURAL NETWORKS: SUCCESS AND FAILURE OF NORMALIZED MARGINS
- 4.33, 0.47, [4, 5, 4] [4, 3, 4]Recovering the Lowest Layer of Deep Networks with High Threshold Activations
- 4.33, 2.05, [2, 4, 7] [5, 3, 3]Mental Fatigue Monitoring using Brain Dynamics Preferences
- 4.33, 0.47, [4, 4, 5] [4, 4, 3]Progressive Weight Pruning Of Deep Neural Networks Using ADMM
- 4.33, 0.47, [4, 4, 5] [3, 4, 4]MixFeat: Mix Feature in Latent Space Learns Discriminative Space
- 4.33, 0.47, [4, 4, 5] [4, 4, 3]The Cakewalk Method
- 4.33, 1.25, [3, 6, 4] [4, 4, 4]On Generalization Bounds of a Family of Recurrent Neural Networks
- 4.33, 1.25, [6, 4, 3] [3, 4, 4]Auto-Encoding Knockoff Generator for FDR Controlled Variable Selection
- 4.33, 0.47, [4, 4, 5] [4, 4, 4]In Your Pace: Learning the Right Example at the Right Time
- 4.33, 0.94, [5, 3, 5] [3, 3, 3]Backdrop: Stochastic Backpropagation
- 4.33, 0.47, [5, 4, 4] [3, 5, 4]SENSE: SEMANTICALLY ENHANCED NODE SEQUENCE EMBEDDING
- 4.33, 0.47, [4, 5, 4] [5, 4, 5]Task-GAN for Improved GAN based Image Restoration
- 4.33, 0.47, [4, 4, 5] [5, 3, 4]EFFICIENT SEQUENCE LABELING WITH ACTOR-CRITIC TRAINING
- 4.33, 1.25, [4, 6, 3] [4, 4, 4]Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks
- 4.33, 0.47, [4, 4, 5] [4, 4, 3]Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-valued Inverse Reinforcement Learning
- 4.33, 0.47, [5, 4, 4] [3, 5, 3]Combining Learned Representations for Combinatorial Optimization
- 4.33, 0.47, [4, 4, 5] [4, 4, 4]From Nodes to Networks: Evolving Recurrent Neural Networks
- 4.33, 0.94, [5, 5, 3] [3, 4, 5]DppNet: Approximating Determinantal Point Processes with Deep Networks
- 4.33, 0.47, [5, 4, 4] [4, 4, 4]Implicit Maximum Likelihood Estimation
- 4.33, 0.47, [5, 4, 4] [4, 4, 4]Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles
- 4.33, 0.47, [4, 4, 5] [5, 4, 4]Asynchronous SGD without gradient delay for efficient distributed training
- 4.33, 0.47, [4, 5, 4] [3, 3, 3]On the effect of the activation function on the distribution of hidden nodes in a deep network
- 4.33, 1.25, [3, 4, 6] [5, 4, 4]Learning Corresponded Rationales for Text Matching
- 4.33, 1.25, [4, 3, 6] [3, 3, 3]REPRESENTATION COMPRESSION AND GENERALIZATION IN DEEP NEURAL NETWORKS
- 4.33, 1.89, [3, 3, 7] [5, 4, 4]Meta-Learning to Guide Segmentation
- 4.33, 1.89, [7, 3, 3] [4, 4, 5]Recycling the discriminator for improving the inference mapping of GAN
- 4.33, 0.47, [5, 4, 4] [4, 4, 5]A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning
- 4.33, 1.25, [4, 6, 3] [4, 4, 4]Neural Probabilistic Motor Primitives for Humanoid Control
- 4.33, 1.70, [5, 2, 6] [3, 4, 3]Dual Learning: Theoretical Study and Algorithmic Extensions
- 4.33, 0.47, [5, 4, 4] [4, 3, 4]Visual Imitation Learning with Recurrent Siamese Networks
- 4.33, 0.47, [4, 5, 4] [5, 3, 3]Learning Hash Codes via Hamming Distance Targets
- 4.33, 0.94, [3, 5, 5] [5, 4, 3]Improving Sample-based Evaluation for Generative Adversarial Networks
- 4.33, 1.25, [6, 3, 4] [4, 5, 1]Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes
- 4.33, 0.47, [4, 5, 4] [5, 4, 3]Successor Uncertainties: exploration and uncertainty in temporal difference learning
- 4.33, 0.47, [4, 4, 5] [5, 3, 4]Jumpout: Improved Dropout for Deep Neural Networks with Rectified Linear Units
- 4.33, 0.47, [4, 4, 5] [5, 4, 4]Pseudosaccades: A simple ensemble scheme for improving classification performance of deep nets
- 4.33, 1.25, [3, 4, 6] [5, 5, 2]Modeling Dynamics of Biological Systems with Deep Generative Neural Networks
- 4.33, 0.47, [5, 4, 4] [5, 4, 5]A SINGLE SHOT PCA-DRIVEN ANALYSIS OF NETWORK STRUCTURE TO REMOVE REDUNDANCY
- 4.33, 0.47, [5, 4, 4] [4, 4, 4]Over-parameterization Improves Generalization in the XOR Detection Problem
- 4.33, 0.47, [4, 4, 5] [4, 4, 5]Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data
- 4.33, 0.47, [4, 4, 5] [4, 3, 4]Rating Continuous Actions in Spatial Multi-Agent Problems
- 4.33, 0.47, [4, 5, 4] [3, 3, 4]Adversarial Examples Are a Natural Consequence of Test Error in Noise
- 4.33, 1.25, [4, 3, 6] [4, 5, 4]Where and when to look? Spatial-temporal attention for action recognition in videos
- 4.33, 0.47, [4, 5, 4] [4, 4, 5]LARGE BATCH SIZE TRAINING OF NEURAL NETWORKS WITH ADVERSARIAL TRAINING AND SECOND-ORDER INFORMATION
- 4.33, 1.25, [6, 3, 4] [5, 4, 1]Teaching to Teach by Structured Dark Knowledge
- 4.33, 0.94, [5, 5, 3] [4, 4, 3]Prototypical Examples in Deep Learning: Metrics, Characteristics, and Utility
- 4.33, 0.47, [4, 4, 5] [4, 5, 4]End-to-End Hierarchical Text Classification with Label Assignment Policy
- 4.33, 1.89, [3, 3, 7] [4, 4, 3]Structured Prediction using cGANs with Fusion Discriminator
- 4.33, 1.25, [6, 4, 3] [5, 4, 4]Open Vocabulary Learning on Source Code with a Graph-Structured Cache
- 4.33, 0.94, [3, 5, 5] [4, 3, 3]Modulated Variational Auto-Encoders for Many-to-Many Musical Timbre Transfer
- 4.33, 0.94, [5, 3, 5] [3, 5, 3]Variational recurrent models for representation learning
- 4.33, 0.94, [5, 3, 5] [4, 5, 4]Inter-BMV: Interpolation with Block Motion Vectors for Fast Semantic Segmentation on Video
- 4.33, 0.47, [4, 4, 5] [4, 4, 4]Do Language Models Have Common Sense?
- 4.33, 0.94, [5, 5, 3] [3, 4, 5]Model-Agnostic Meta-Learning for Multimodal Task Distributions
- 4.33, 0.47, [4, 5, 4] [4, 3, 4]How Training Data Affect the Accuracy and Robustness of Neural Networks for Image Classification
- 4.33, 1.25, [3, 6, 4] [5, 2, 5]Locally Linear Unsupervised Feature Selection
- 4.33, 0.47, [5, 4, 4] [4, 4, 3]SALSA-TEXT : SELF ATTENTIVE LATENT SPACE BASED ADVERSARIAL TEXT GENERATION
- 4.33, 0.47, [4, 5, 4] [5, 5, 5]Harmonic Unpaired Image-to-image Translation
- 4.33, 1.25, [6, 3, 4] [3, 4, 4]On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models
- 4.33, 0.94, [5, 5, 3] [3, 4, 1]Meta-Learning Neural Bloom Filters
- 4.33, 0.47, [4, 4, 5] [4, 4, 3]BlackMarks: Black-box Multi-bit Watermarking for Deep Neural Networks
- 4.33, 1.25, [3, 6, 4] [5, 4, 4]Optimal Attacks against Multiple Classifiers
- 4.33, 0.47, [4, 4, 5] [4, 4, 2]Evolutionary-Neural Hybrid Agents for Architecture Search
- 4.33, 1.89, [7, 3, 3] [2, 4, 4]On Inductive Biases in Deep Reinforcement Learning
- 4.33, 1.25, [4, 3, 6] [3, 4, 3]W2GAN: RECOVERING AN OPTIMAL TRANSPORTMAP WITH A GAN
- 4.33, 0.94, [5, 5, 3] [2, 4, 4]Latent Transformations for Object View Points Synthesis
- 4.33, 1.25, [4, 3, 6] [3, 5, 4]Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
- 4.33, 0.47, [4, 5, 4] [3, 3, 3]Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning
- 4.33, 0.94, [3, 5, 5] [4, 2, 4]Multi-Objective Value Iteration with Parameterized Threshold-Based Safety Constraints
- 4.33, 0.47, [5, 4, 4] [4, 2, 4]Select Via Proxy: Efficient Data Selection For Training Deep Networks
- 4.33, 0.47, [5, 4, 4] [3, 5, 3]Variational Domain Adaptation
- 4.33, 0.47, [4, 5, 4] [4, 5, 5]COMPOSITION AND DECOMPOSITION OF GANS
- 4.33, 0.94, [5, 5, 3] [4, 5, 4]PIE: Pseudo-Invertible Encoder
- 4.33, 0.47, [5, 4, 4] [4, 4, 2]TopicGAN: Unsupervised Text Generation from Explainable Latent Topics
- 4.33, 0.47, [4, 5, 4] [3, 3, 4]NICE: noise injection and clamping estimation for neural network quantization
- 4.33, 0.94, [5, 3, 5] [3, 5, 5]Network Reparameterization for Unseen Class Categorization
- 4.33, 0.94, [3, 5, 5] [4, 3, 3]Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning
- 4.33, 1.25, [4, 3, 6] [4, 3, 4]Architecture Compression
- 4.33, 1.89, [3, 7, 3] [3, 3, 2]A model cortical network for spatiotemporal sequence learning and prediction
- 4.33, 0.47, [4, 4, 5] [4, 3, 2]Modulating transfer between tasks in gradient-based meta-learning
- 4.33, 0.47, [4, 4, 5] [3, 4, 3]Mean Replacement Pruning
- 4.33, 0.47, [4, 5, 4] [4, 5, 3]Stochastic Quantized Activation: To prevent Overfitting in Fast Adversarial Training
- 4.33, 0.94, [5, 3, 5] [3, 4, 3]Provable Defenses against Spatially Transformed Adversarial Inputs: Impossibility and Possibility Results
- 4.33, 0.94, [5, 3, 5] [5, 3, 4]Learning Physics Priors for Deep Reinforcement Learing
- 4.33, 1.25, [3, 4, 6] [5, 4, 3]Looking inside the black box: assessing the modular structure of deep generative models with counterfactuals
- 4.33, 0.47, [5, 4, 4] [4, 4, 5]Correction Networks: Meta-Learning for Zero-Shot Learning
- 4.33, 0.94, [5, 5, 3] [2, 3, 5]Assessing Generalization in Deep Reinforcement Learning
- 4.33, 0.94, [5, 3, 5] [4, 3, 4]Bridging HMMs and RNNs through Architectural Transformations
- 4.33, 0.47, [4, 4, 5] [4, 2, 4]Variadic Learning by Bayesian Nonparametric Deep Embedding
- 4.25, 0.43, [5, 4, 4, 4] [3, 4, 5, 2]Characterizing the Accuracy/Complexity Landscape of Explanations of Deep Networks through Knowledge Extraction
- 4.25, 0.43, [5, 4, 4, 4] [3, 4, 3, 3]A Priori Estimates of the Generalization Error for Two-layer Neural Networks
- 4.25, 0.43, [5, 4, 4, 4] [3, 4, 4, 5]Countdown Regression: Sharp and Calibrated Survival Predictions
- 4.25, 1.48, [2, 4, 6, 5] [4, 3, 4, 3]Understanding the Asymptotic Performance of Model-Based RL Methods
- 4.25, 0.43, [4, 5, 4, 4] [4, 4, 3, 4]Unlabeled Disentangling of GANs with Guided Siamese Networks
- 4.25, 0.43, [4, 4, 4, 5] [4, 5, 5, 4]Discovering General-Purpose Active Learning Strategies
- 4.00, 0.00, [4, 4, 4] [5, 4, 4]Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourages convex latent distributions
- 4.00, 0.82, [4, 3, 5] [4, 5, 5]The Forward-Backward Embedding of Directed Graphs
- 4.00, 1.41, [6, 3, 3] [4, 5, 3]Large-scale classification of structured objects using a CRF with deep class embedding
- 4.00, 0.00, [4, 4, 4] [5, 4, 4]Overcoming catastrophic forgetting through weight consolidation and long-term memory
- 4.00, 0.82, [4, 3, 5] [4, 4, 5]Neural Network Cost Landscapes as Quantum States
- 4.00, 0.82, [5, 3, 4] [4, 4, 5]Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers
- 4.00, 0.82, [4, 3, 5] [3, 5, 2]Learning Latent Semantic Representation from Pre-defined Generative Model
- 4.00, 0.00, [4, 4, 4] [5, 4, 3]HC-Net: Memory-based Incremental Dual-Network System for Continual learning
- 4.00, 0.00, [4, 4, 4] [4, 4, 5]Sequence Modelling with Memory-Augmented Recurrent Neural Networks
- 4.00, 0.82, [3, 5, 4] [4, 3, 4]MERCI: A NEW METRIC TO EVALUATE THE CORRELATION BETWEEN PREDICTIVE UNCERTAINTY AND TRUE ERROR
- 4.00, 0.00, [4, 4] [1, 2]S-System, Geometry, Learning, and Optimization: A Theory of Neural Networks
- 4.00, 0.82, [3, 4, 5] [4, 3, 4]Difference-Seeking Generative Adversarial Network
- 4.00, 0.82, [5, 4, 3] [4, 4, 4]Semantic Parsing via Cross-Domain Schema
- 4.00, 0.82, [5, 4, 3] [4, 4, 5]On the Selection of Initialization and Activation Function for Deep Neural Networks
- 4.00, 0.00, [4, 4, 4] [3, 4, 3]Deep processing of structured data
- 4.00, 0.82, [3, 5, 4] [4, 4, 4]Better Accuracy with Quantified Privacy: Representations Learned via Reconstructive Adversarial Network
- 4.00, 0.82, [5, 4, 3] [3, 4, 3]Modular Deep Probabilistic Programming
- 4.00, 0.00, [4, 4, 4] [3, 5, 4]Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning
- 4.00, 0.82, [5, 4, 3] [4, 5, 4]A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing
- 4.00, 0.82, [4, 3, 5] [4, 4, 4]Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
- 4.00, 0.82, [5, 3, 4] [4, 4, 4]Assumption Questioning: Latent Copying and Reward Exploitation in Question Generation
- 4.00, 0.82, [4, 3, 5] [5, 4, 3]Polar Prototype Networks
- 4.00, 0.82, [3, 5, 4] [4, 4, 5]Applications of Gaussian Processes in Finance
- 4.00, 0.82, [5, 4, 3] [4, 4, 4]Incremental Hierarchical Reinforcement Learning with Multitask LMDPs
- 4.00, 0.82, [3, 4, 5] [3, 4, 3]On the Statistical and Information Theoretical Characteristics of DNN Representations
- 4.00, 0.00, [4, 4, 4] [4, 5, 4]Explaining Neural Networks Semantically and Quantitatively
- 4.00, 1.41, [6, 3, 3] [3, 3, 3]microGAN: Promoting Variety through Microbatch Discrimination
- 4.00, 0.82, [5, 3, 4] [4, 5, 2]PA-GAN: Improving GAN Training by Progressive Augmentation
- 4.00, 0.00, [4, 4, 4] [4, 3, 2]Deep Generative Models for learning Coherent Latent Representations from Multi-Modal Data
- 4.00, 0.82, [3, 5, 4] [4, 3, 4]Overfitting Detection of Deep Neural Networks without a Hold Out Set
- 4.00, 0.00, [4, 4, 4] [3, 4, 5]Mol-CycleGAN – a generative model for molecular optimization
- 4.00, 0.00, [4, 4, 4] [4, 4, 4]NUTS: Network for Unsupervised Telegraphic Summarization
- 4.00, 0.00, [4, 4, 4] [4, 4, 3]Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning
- 4.00, 0.82, [4, 5, 3] [4, 3, 4]Few-shot Classification on Graphs with Structural Regularized GCNs
- 4.00, 0.82, [3, 5, 4] [5, 3, 5]Second-Order Adversarial Attack and Certifiable Robustness
- 4.00, 1.63, [4, 2, 6] [3, 5, 2]Reinforcement Learning: From temporal to spatial value decomposition
- 4.00, 0.00, [4, 4, 4] [4, 4, 5]EXPLORATION OF EFFICIENT ON-DEVICE ACOUSTIC MODELING WITH NEURAL NETWORKS
- 4.00, 1.41, [5, 2, 5] [5, 4, 4]The effectiveness of layer-by-layer training using the information bottleneck principle
- 4.00, 0.82, [3, 5, 4] [4, 3, 3]Layerwise Recurrent Autoencoder for General Real-world Traffic Flow Forecasting
- 4.00, 0.82, [3, 4, 5] [4, 4, 5]ON THE USE OF CONVOLUTIONAL AUTO-ENCODER FOR INCREMENTAL CLASSIFIER LEARNING IN CONTEXT AWARE ADVERTISEMENT
- 4.00, 0.00, [4, 4, 4] [4, 4, 4]ChainGAN: A sequential approach to GANs
- 4.00, 0.00, [4, 4, 4] [4, 5, 5]Activity Regularization for Continual Learning
- 4.00, 0.82, [5, 4, 3] [3, 4, 5]Robustness and Equivariance of Neural Networks
- 4.00, 0.82, [5, 4, 3] [4, 4, 3]Distributionally Robust Optimization Leads to Better Generalization: on SGD and Beyond
- 4.00, 0.82, [5, 3, 4] [4, 4, 4]D2KE: From Distance to Kernel and Embedding via Random Features For Structured Inputs
- 4.00, 0.00, [4, 4, 4] [4, 4, 5]Hyper-Regularization: An Adaptive Choice for the Learning Rate in Gradient Descent
- 4.00, 0.82, [4, 5, 3] [3, 5, 5]Complexity of Training ReLU Neural Networks
- 4.00, 1.41, [2, 4, 4, 6] [5, 4, 2, 2]Efficient Exploration through Bayesian Deep Q-Networks
- 4.00, 0.82, [4, 5, 3] [4, 5, 4]Sequenced-Replacement Sampling for Deep Learning
- 4.00, 0.00, [4, 4, 4] [4, 5, 5]DEEP ADVERSARIAL FORWARD MODEL
- 4.00, 0.82, [5, 4, 3] [3, 4, 4]Look Ma, No GANs! Image Transformation with ModifAE
- 4.00, 0.82, [4, 5, 3] [3, 3, 4]Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
- 4.00, 0.82, [5, 4, 3] [4, 4, 5]ACIQ: Analytical Clipping for Integer Quantization of neural networks
- 4.00, 0.82, [5, 4, 3] [4, 3, 4]Constrained Bayesian Optimization for Automatic Chemical Design
- 4.00, 0.82, [3, 5, 4] [4, 3, 5]The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions
- 4.00, 0.00, [4, 4, 4] [4, 3, 4]Co-manifold learning with missing data
- 4.00, 0.00, [4, 4] [5, 4]Fast Binary Functional Search on Graph
- 4.00, 0.82, [3, 4, 5] [4, 3, 4]Towards More Theoretically-Grounded Particle Optimization Sampling for Deep Learning
- 4.00, 0.00, [4, 4, 4] [4, 3, 4]Differentially Private Federated Learning: A Client Level Perspective
- 4.00, 0.82, [3, 5, 4] [4, 2, 4]UaiNets: From Unsupervised to Active Deep Anomaly Detection
- 4.00, 0.82, [5, 4, 3] [4, 4, 4]Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy
- 4.00, 0.82, [5, 3, 4] [3, 4, 3]In search of theoretically grounded pruning
- 4.00, 1.63, [2, 4, 6] [5, 3, 5]Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
- 4.00, 0.82, [3, 4, 5] [5, 4, 4]Learning Representations in Model-Free Hierarchical Reinforcement Learning
- 4.00, 0.82, [5, 3, 4] [4, 5, 4]Dual Importance Weight GAN
- 4.00, 0.00, [4, 4, 4] [5, 5, 4]Relational Graph Attention Networks
- 4.00, 0.00, [4, 4, 4] [3, 4, 5]HyperGAN: Exploring the Manifold of Neural Networks
- 4.00, 0.82, [4, 3, 5] [5, 5, 3]Generalized Capsule Networks with Trainable Routing Procedure
- 4.00, 0.82, [4, 3, 5] [2, 2, 4]Distilled Agent DQN for Provable Adversarial Robustness
- 4.00, 0.00, [4, 4, 4] [4, 5, 4]Distinguishability of Adversarial Examples
- 4.00, 1.41, [6, 3, 3] [4, 5, 4]Iteratively Learning from the Best
- 4.00, 0.82, [5, 3, 4] [3, 4, 3]Evaluating GANs via Duality
- 4.00, 0.82, [4, 3, 5] [3, 4, 4]Constraining Action Sequences with Formal Languages for Deep Reinforcement Learning
- 4.00, 0.82, [4, 3, 5] [5, 5, 4]Overlapping Community Detection with Graph Neural Networks
- 4.00, 0.82, [4, 5, 3] [4, 3, 5]DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
- 4.00, 0.00, [4, 4, 4] [5, 4, 4]Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding
- 4.00, 0.82, [4, 5, 3] [4, 3, 3]Prob2Vec: Mathematical Semantic Embedding for Problem Retrieval in Adaptive Tutoring
- 4.00, 2.16, [5, 1, 6] [4, 4, 4]Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets
- 4.00, 0.82, [4, 3, 5] [4, 3, 4]Reconciling Feature-Reuse and Overfitting in DenseNet with Specialized Dropout
- 4.00, 0.82, [3, 5, 4] [4, 5, 4]N/A
- 4.00, 0.82, [5, 4, 3] [5, 4, 5]Training Hard-Threshold Networks with Combinatorial Search in a Discrete Target Propagation Setting
- 4.00, 0.00, [4, 4, 4] [4, 4, 4]Latent Domain Transfer: Crossing modalities with Bridging Autoencoders
- 4.00, 0.82, [4, 3, 5] [5, 4, 3]Neural Regression Tree
- 4.00, 0.82, [5, 3, 4] [5, 2, 4]Neural MMO: A massively multiplayer game environment for intelligent agents
- 4.00, 0.00, [4, 4, 4] [4, 4, 4]Uncertainty-guided Lifelong Learning in Bayesian Networks
- 4.00, 0.00, [4, 4, 4] [5, 3, 5]Language Modeling with Graph Temporal Convolutional Networks
- 4.00, 0.82, [4, 5, 3] [4, 5, 5]RNNs with Private and Shared Representations for Semi-Supervised Sequence Learning
- 4.00, 1.41, [2, 5, 5] [2, 2, 3]Universal discriminative quantum neural networks
- 4.00, 0.00, [4, 4, 4] [4, 4, 5]Learning to Search Efficient DenseNet with Layer-wise Pruning
- 4.00, 0.82, [3, 5, 4] [5, 4, 5]Understanding Opportunities for Efficiency in Single-image Super Resolution Networks
- 4.00, 0.82, [3, 4, 5] [3, 5, 4]Q-map: a Convolutional Approach for Goal-Oriented Reinforcement Learning
- 4.00, 0.82, [3, 5, 4] [5, 4, 4]Deepström Networks
- 4.00, 0.82, [4, 3, 5] [4, 3, 4]Pearl: Prototype lEArning via Rule Lists
- 4.00, 0.82, [3, 5, 4] [4, 2, 5]Reinforced Pipeline Optimization: Behaving Optimally with Non-Differentiabilities
- 4.00, 0.00, [4, 4, 4] [4, 4, 4]Trajectory VAE for multi-modal imitation
- 4.00, 0.00, [4, 4, 4] [3, 4, 5]DATA POISONING ATTACK AGAINST NODE EMBEDDING METHODS
- 4.00, 0.00, [4, 4, 4] [4, 3, 4]Unsupervised Exploration with Deep Model-Based Reinforcement Learning
- 4.00, 1.63, [2, 6, 4] [4, 4, 3]On the Trajectory of Stochastic Gradient Descent in the Information Plane
- 4.00, 0.82, [5, 4, 3] [2, 4, 3]Functional Bayesian Neural Networks for Model Uncertainty Quantification
- 4.00, 1.63, [6, 2, 4] [4, 4, 4]REVISTING NEGATIVE TRANSFER USING ADVERSARIAL LEARNING
- 4.00, 0.00, [4, 4, 4] [3, 4, 4]Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor
- 4.00, 0.00, [4, 4, 4] [5, 3, 4]Ain’t Nobody Got Time for Coding: Structure-Aware Program Synthesis from Natural Language
- 4.00, 0.00, [4, 4, 4] [4, 3, 4]Graph Generation via Scattering
- 4.00, 1.41, [3, 3, 6] [2, 5, 4]Improving machine classification using human uncertainty measurements
- 4.00, 0.82, [3, 4, 5] [4, 5, 3]Empirically Characterizing Overparameterization Impact on Convergence
- 4.00, 0.00, [4, 4, 4] [4, 5, 4]Continual Learning via Explicit Structure Learning
- 3.67, 1.25, [5, 2, 4] [5, 4, 4]R ESIDUAL NETWORKS CLASSIFY INPUTS BASED ON THEIR NEURAL TRANSIENT DYNAMICS
- 3.67, 0.47, [4, 3, 4] [5, 3, 4]Diminishing Batch Normalization
- 3.67, 1.70, [6, 3, 2] [3, 4, 1]Filter Training and Maximum Response: Classification via Discerning
- 3.67, 1.25, [4, 2, 5] [4, 4, 5]Optimizing for Generalization in Machine Learning with Cross-Validation Gradients
- 3.67, 0.47, [3, 4, 4] [3, 4, 3]Image Score: how to select useful samples
- 3.67, 0.47, [3, 4, 4] [3, 4, 2]Feature Attribution As Feature Selection
- 3.67, 1.25, [5, 4, 2] [3, 5, 5]Discrete Structural Planning for Generating Diverse Translations
- 3.67, 0.47, [4, 4, 3] [3, 4, 4]DynCNN: An Effective Dynamic Architecture on Convolutional Neural Network for Surveillance Videos
- 3.67, 0.47, [4, 3, 4] [4, 5, 3]An Attention-Based Model for Learning Dynamic Interaction Networks
- 3.67, 2.49, [3, 1, 7] [4, 5, 3]Optimization on Multiple Manifolds
- 3.67, 0.47, [3, 4, 4] [4, 5, 4]RETHINKING SELF-DRIVING : MULTI -TASK KNOWLEDGE FOR BETTER GENERALIZATION AND ACCIDENT EXPLANATION ABILITY
- 3.67, 2.49, [1, 7, 3] [5, 3, 4]Why Do Neural Response Generation Models Prefer Universal Replies?
- 3.67, 0.47, [4, 3, 4] [4, 4, 4]DelibGAN: Coarse-to-Fine Text Generation via Adversarial Network
- 3.67, 0.47, [3, 4, 4] [4, 5, 4]Encoding Category Trees Into Word-Embeddings Using Geometric Approach
- 3.67, 0.94, [3, 3, 5] [5, 5, 4]GradMix: Multi-source Transfer across Domains and Tasks
- 3.67, 0.47, [3, 4, 4] [5, 4, 3]Synthnet: Learning synthesizers end-to-end
- 3.67, 0.47, [4, 4, 3] [5, 4, 4]Prior Networks for Detection of Adversarial Attacks
- 3.67, 0.94, [3, 3, 5] [5, 4, 3]Localized random projections challenge benchmarks for bio-plausible deep learning
- 3.67, 0.94, [3, 5, 3] [2, 2, 3]A fully automated periodicity detection in time series
- 3.67, 0.47, [4, 4, 3] [5, 4, 4]Generating Images from Sounds Using Multimodal Features and GANs
- 3.67, 0.94, [5, 3, 3] [4, 5, 4]Text Embeddings for Retrieval from a Large Knowledge Base
- 3.67, 0.47, [4, 4, 3] [5, 4, 5]Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
- 3.67, 0.47, [3, 4, 4] [3, 4, 4]Riemannian Stochastic Gradient Descent for Tensor-Train Recurrent Neural Networks
- 3.67, 0.47, [4, 4, 3] [4, 3, 4]Learning agents with prioritization and parameter noise in continuous state and action space
- 3.67, 0.47, [4, 3, 4] [3, 5, 4]Hierarchical Attention: What Really Counts in Various NLP Tasks
- 3.67, 0.47, [3, 4, 4] [4, 3, 4]Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks
- 3.67, 0.47, [4, 3, 4] [4, 2, 4]Using Deep Siamese Neural Networks to Speed up Natural Products Research
- 3.67, 0.47, [4, 3, 4] [3, 5, 4]Graph Spectral Regularization For Neural Network Interpretability
- 3.67, 0.47, [4, 4, 3] [4, 3, 5]Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning
- 3.67, 0.47, [4, 4, 3] [2, 4, 4]Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks
- 3.67, 0.47, [4, 3, 4] [5, 5, 4]Question Generation using a Scratchpad Encoder
- 3.67, 0.47, [3, 4, 4] [4, 4, 4]Adversarially Robust Training through Structured Gradient Regularization
- 3.67, 0.47, [3, 4, 4] [5, 4, 3]GEOMETRIC AUGMENTATION FOR ROBUST NEURAL NETWORK CLASSIFIERS
- 3.67, 1.25, [2, 5, 4] [4, 3, 4]DEEP HIERARCHICAL MODEL FOR HIERARCHICAL SELECTIVE CLASSIFICATION AND ZERO SHOT LEARNING
- 3.67, 0.47, [4, 4, 3] [4, 5, 4]Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
- 3.67, 0.47, [4, 3, 4] [5, 4, 3]Feature Transformers: A Unified Representation Learning Framework for Lifelong Learning
- 3.67, 0.47, [3, 4, 4] [4, 4, 5]Normalization Gradients are Least-squares Residuals
- 3.67, 0.47, [4, 3, 4] [5, 4, 4]DEEP GEOMETRICAL GRAPH Classification WITH DYNAMIC POOLING
- 3.67, 1.25, [4, 2, 5] [4, 5, 4]Differentiable Greedy Networks
- 3.67, 1.25, [5, 2, 4] [4, 5, 4]Kmer2vec: Towards transcriptomic representations by learning kmer embeddings
- 3.67, 0.47, [4, 3, 4] [4, 4, 5]Graph Learning Network: A Structure Learning Algorithm
- 3.67, 0.47, [3, 4, 4] [3, 5, 4]Controlling Over-generalization and its Effect on Adversarial Examples Detection and Generation
- 3.67, 0.47, [4, 3, 4] [4, 4, 4]PCNN: Environment Adaptive Model Without Finetuning
- 3.67, 0.47, [3, 4, 4] [4, 4, 5]Optimized Gated Deep Learning Architectures for Sensor Fusion
- 3.67, 0.47, [3, 4, 4] [4, 3, 5]A Walk with SGD: How SGD Explores Regions of Deep Network Loss?
- 3.67, 0.94, [3, 3, 5] [4, 4, 3]Automatic generation of object shapes with desired functionalities
- 3.67, 0.47, [4, 4, 3] [4, 5, 5]Dynamic Recurrent Language Model
- 3.67, 0.94, [3, 5, 3] [5, 1, 4]D-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation
- 3.67, 0.47, [3, 4, 4] [4, 3, 4]Inhibited Softmax for Uncertainty Estimation in Neural Networks
- 3.67, 0.47, [4, 4, 3] [5, 5, 4]Unsupervised Video-to-Video Translation
- 3.67, 0.47, [3, 4, 4] [4, 3, 4]Efficient Federated Learning via Variational Dropout
- 3.67, 0.47, [4, 3, 4] [5, 5, 4]Contextual Recurrent Convolutional Model for Robust Visual Learning
- 3.67, 0.47, [4, 4, 3] [4, 4, 4]Unsupervised one-to-many image translation
- 3.67, 0.47, [3, 4, 4] [4, 3, 4]INTERPRETABLE CONVOLUTIONAL FILTER PRUNING
- 3.67, 0.94, [3, 3, 5] [5, 4, 3]Fake Sentence Detection as a Training Task for Sentence Encoding
- 3.67, 0.47, [4, 4, 3] [5, 3, 3]Accelerating first order optimization algorithms
- 3.67, 0.94, [3, 3, 5] [4, 4, 4]The Natural Language Decathlon: Multitask Learning as Question Answering
- 3.50, 1.12, [5, 2, 3, 4] [2, 5, 2, 3]Learning to Reinforcement Learn by Imitation
- 3.50, 0.50, [3, 3, 4, 4] [4, 2, 4, 3]LSH Microbatches for Stochastic Gradients: Value in Rearrangement
- 3.33, 0.47, [4, 3, 3] [3, 4, 4]Linearizing Visual Processes with Deep Generative Models
- 3.33, 0.47, [3, 3, 4] [4, 4, 3]Interpreting Layered Neural Networks via Hierarchical Modular Representation
- 3.33, 0.94, [4, 2, 4] [5, 4, 3]IEA: Inner Ensemble Average within a convolutional neural network
- 3.33, 0.47, [3, 3, 4] [4, 4, 4]Accidental exploration through value predictors
- 3.33, 0.47, [3, 3, 4] [5, 4, 3]Learning and Data Selection in Big Datasets
- 3.33, 0.47, [3, 3, 4] [4, 5, 4]Human Action Recognition Based on Spatial-Temporal Attention
- 3.33, 0.47, [3, 3, 4] [5, 3, 4]SHE2: Stochastic Hamiltonian Exploration and Exploitation for Derivative-Free Optimization
- 3.33, 0.47, [3, 4, 3] [5, 4, 4]Encoder Discriminator Networks for Unsupervised Representation Learning
- 3.33, 0.47, [4, 3, 3] [4, 4, 5]Understanding and Improving Sequence-Labeling NER with Self-Attentive LSTMs
- 3.33, 1.25, [3, 5, 2] [4, 5, 5]Geometric Operator Convolutional Neural Network
- 3.33, 0.47, [3, 4, 3] [5, 4, 5]Multi-Scale Stacked Hourglass Network for Human Pose Estimation
- 3.33, 0.47, [3, 4, 3] [5, 4, 5]A quantifiable testing of global translational invariance in Convolutional and Capsule Networks
- 3.33, 0.47, [3, 4, 3] [5, 5, 4]MAJOR-MINOR LSTMS FOR WORD-LEVEL LANGUAGE MODEL
- 3.33, 0.47, [3, 3, 4] [4, 4, 4]Deep models calibration with bayesian neural networks
- 3.33, 0.94, [4, 4, 2] [4, 3, 4]BIGSAGE: unsupervised inductive representation learning of graph via bi-attended sampling and global-biased aggregating
- 3.33, 1.25, [3, 2, 5] [4, 5, 3]Gradient Acceleration in Activation Functions
- 3.33, 0.47, [4, 3, 3] [5, 5, 4]BEHAVIOR MODULE IN NEURAL NETWORKS
- 3.33, 0.47, [3, 4, 3] [4, 3, 4]Neural Random Projections for Language Modelling
- 3.33, 0.47, [3, 4, 3] [4, 4, 5]Step-wise Sensitivity Analysis: Identifying Partially Distributed Representations for Interpretable Deep Learning
- 3.33, 0.94, [2, 4, 4] [4, 4, 3]Deconfounding Reinforcement Learning
- 3.33, 0.94, [2, 4, 4] [5, 4, 4]Detecting Topological Defects in 2D Active Nematics Using Convolutional Neural Networks
- 3.33, 0.47, [3, 3, 4] [3, 4, 5]Neural Distribution Learning for generalized time-to-event prediction
- 3.33, 0.47, [4, 3, 3] [3, 4, 5]Beyond Games: Bringing Exploration to Robots in Real-world
- 3.33, 0.47, [3, 4, 3] [4, 5, 4]Empirical Study of Easy and Hard Examples in CNN Training
- 3.33, 1.70, [1, 5, 4] [4, 3, 4]Deterministic Policy Gradients with General State Transitions
- 3.33, 0.47, [4, 3, 3] [4, 5, 4]Neural Network Regression with Beta, Dirichlet, and Dirichlet-Multinomial Outputs
- 3.33, 0.47, [3, 3, 4] [2, 4, 3]ATTACK GRAPH CONVOLUTIONAL NETWORKS BY ADDING FAKE NODES
- 3.33, 1.25, [3, 2, 5] [4, 5, 2]Generative model based on minimizing exact empirical Wasserstein distance
- 3.33, 1.25, [5, 2, 3] [3, 2, 5]Learning powerful policies and better dynamics models by encouraging consistency
- 3.33, 0.47, [3, 4, 3] [5, 3, 4]Non-Synergistic Variational Autoencoders
- 3.33, 1.25, [5, 2, 3] [3, 5, 5]Uncertainty in Multitask Transfer Learning
- 3.33, 1.25, [5, 2, 3] [3, 4, 3]The Conditional Entropy Bottleneck
- 3.33, 0.47, [4, 3, 3] [3, 3, 4]Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
- 3.33, 0.47, [4, 3, 3] [4, 2, 4]Combining adaptive algorithms and hypergradient method: a performance and robustness study
- 3.00, 0.82, [2, 4, 3] [4, 4, 5]ATTENTION INCORPORATE NETWORK: A NETWORK CAN ADAPT VARIOUS DATA SIZE
- 3.00, 0.00, [3, 3, 3] [5, 4, 3]Nonlinear Channels Aggregation Networks for Deep Action Recognition
- 3.00, 0.82, [4, 2, 3] [5, 5, 4]Hybrid Policies Using Inverse Rewards for Reinforcement Learning
- 3.00, 0.82, [2, 4, 3] [4, 4, 5]An Exhaustive Analysis of Lazy vs. Eager Learning Methods for Real-Estate Property Investment
- 3.00, 0.82, [2, 4, 3] [5, 5, 5]Stacking for Transfer Learning
- 3.00, 0.00, [3, 3, 3] [5, 3, 4]Mapping the hyponymy relation of wordnet onto vector Spaces
- 3.00, 0.82, [2, 4, 3] [5, 4, 4]ReNeg and Backseat Driver: Learning from demonstration with continuous human feedback
- 3.00, 0.00, [3, 3, 3] [3, 4, 3]Real-time Neural-based Input Method
- 3.00, 1.00, [4, 2] [5, 4]FROM DEEP LEARNING TO DEEP DEDUCING: AUTOMATICALLY TRACKING DOWN NASH EQUILIBRIUM THROUGH AUTONOMOUS NEURAL AGENT, A POSSIBLE MISSING STEP TOWARD GENERAL A.I.
- 3.00, 0.82, [4, 2, 3] [4, 2, 1]Learning of Sophisticated Curriculums by viewing them as Graphs over Tasks
- 3.00, 0.00, [3, 3, 3] [5, 4, 5]iRDA Method for Sparse Convolutional Neural Networks
- 3.00, 0.82, [3, 4, 2] [2, 4, 5]Geometry of Deep Convolutional Networks
- 3.00, 0.82, [4, 2, 3] [5, 4, 4]Calibration of neural network logit vectors to combat adversarial attacks
- 3.00, 0.82, [2, 4, 3] [4, 2, 4]Probabilistic Program Induction for Intuitive Physics Game Play
- 3.00, 0.00, [3, 3, 3] [4, 3, 2]An Analysis of Composite Neural Network Performance from Function Composition Perspective
- 3.00, 0.00, [3, 3, 3] [3, 2, 4]Dopamine: A Research Framework for Deep Reinforcement Learning
- 3.00, 0.82, [3, 2, 4] [4, 4, 4]Learning with Reflective Likelihoods
- 3.00, 1.41, [4, 1, 4] [5, 4, 3]Variational Autoencoders for Text Modeling without Weakening the Decoder
- 3.00, 0.82, [4, 3, 2] [4, 3, 4]Evaluation Methodology for Attacks Against Confidence Thresholding Models
- 3.00, 0.00, [3, 3, 3] [4, 3, 3]A NON-LINEAR THEORY FOR SENTENCE EMBEDDING
- 3.00, 0.82, [4, 3, 2] [4, 3, 5]Learn From Neighbour: A Curriculum That Train Low Weighted Samples By Imitating
- 3.00, 0.00, [3, 3, 3] [4, 4, 4]One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy
- 3.00, 0.82, [4, 3, 2] [2, 3, 4]Feature quantization for parsimonious and interpretable predictive models
- 3.00, 0.00, [3, 3, 3] [4, 5, 4]Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference
- 3.00, 0.82, [2, 3, 4] [5, 4, 4]HR-TD: A Regularized TD Method to Avoid Over-Generalization
- 3.00, 0.82, [4, 3, 2] [4, 4, 1]HANDLING CONCEPT DRIFT IN WIFI-BASED INDOOR LOCALIZATION USING REPRESENTATION LEARNING
- 3.00, 0.82, [2, 3, 4] [3, 3, 4]A Rate-Distortion Theory of Adversarial Examples
- 3.00, 0.82, [2, 3, 4] [5, 5, 3]Classification in the dark using tactile exploration
- 3.00, 0.00, [3, 3, 3] [4, 5, 4]End-to-End Multi-Lingual Multi-Speaker Speech Recognition
- 3.00, 0.82, [2, 3, 4] [4, 5, 5]A Self-Supervised Method for Mapping Human Instructions to Robot Policies
- 3.00, 0.82, [2, 3, 4] [3, 4, 4]ATTENTIVE EXPLAINABILITY FOR PATIENT TEMPO- RAL EMBEDDING
- 3.00, 0.00, [3, 3, 3] [4, 5, 5]From Amortised to Memoised Inference: Combining Wake-Sleep and Variational-Bayes for Unsupervised Few-Shot Program Learning
- 2.75, 0.83, [4, 2, 3, 2] [5, 5, 3, 4]Predictive Local Smoothness for Stochastic Gradient Methods
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