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Unsupervised learning of object frames by dense equivariant image labelling. One-Sided Unsupervised Domain Mapping. Contrastive Learning for Image Captioning. Dynamic Routing Between Capsules. Label Distribution Learning Forests. An inner-loop free solution to inverse problems using deep neural networks.

Structured Embedding Models for Grouped Data. Wider and Deeper, Cheaper and Faster: Fast-Slow Recurrent Neural Networks. Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model.

Encoding the Future into Recurrent Networks. Deep Learning for Planning under Partial Observability. Neural Discrete Representation Learning. Variational Memory Addressing in Generative Models. Cortical microcircuits as gated-recurrent neural networks. Continual Learning with Deep Generative Replay.

Hierarchical Attentive Recurrent Tracking. Learning to Inpaint for Image Compression. Learning a Physics Simulator from Video. Learning Discovery Thresholds from Hypothesis Features. Eigen-Distortions of Hierarchical Representations. Learning Affinity via Spatial Propagation Networks. Few-Shot Adversarial Domain Adaptation. Riemannian approach to batch normalization. How regularization affects the critical points in linear networks. Compression-aware Training of Deep Networks. Exploring Generalization in Deep Learning.

Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization. Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations.

Effective brain connectivity with causal kernels. Interpreting Neural Networks on the Fly. Decomposable Submodular Function Minimization: Robust Optimization for Non-Convex Objectives.

On the Optimization Landscape of Tensor Decompositions. Implicit Regularization in Matrix Factorization. Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration. Nonlinear Acceleration of Stochastic Algorithms.

Acceleration and Averaging in Stochastic Descent Dynamics. Reducing Reparameterization Gradient Variance. Parallel Streaming Wasserstein Barycenters. An algorithm for measuring the accuracy of probabilistic inference algorithms.

Streaming Sparse Gaussian Process Approximations. Linearly constrained Gaussian processes. Linear regression without correspondence. On the Complexity of Learning Neural Networks. Adaptive Clustering through Semidefinite Programming. Learning with Average Top-k Loss. A graph-theoretic approach to multitasking. Information-theoretic analysis of generalization capability of learning algorithms. Independence clustering without a matrix.

Convergence, Limit Cycles and Chaos. Deep Reinforcement Learning from Human Preferences. Active Exploration for Learning Symbolic Representations. Successor Features for Transfer in Reinforcement Learning. Learning Unknown Markov Decision Processes: A Thompson Sampling Approach. Reinforcement Learning under Model Mismatch. Action Centered Contextual Bandits.

Conservative Contextual Linear Bandits. Multi-Task Learning for Contextual Bandits. Boltzmann Exploration Done Right. Improving the Expected Improvement Algorithm. Scalable Generalized Linear Bandits: Online Computation and Hashing. Hypothesis Transfer Learning via Transformation Functions. Learning multiple visual domains with residual adapters.

Nonparametric Online Regression while Learning the Metric. Online Convex Optimization with Stochastic Constraints. Online Prediction with Selfish Experts. Practical Locally Private Heavy Hitters. Generating steganographic images via adversarial training.

On Fairness and Calibration. Avoiding Discrimination through Causal Reasoning. Optimized Pre-Processing for Discrimination Prevention. Fairness Objectives for Collaborative Filtering. Off-policy evaluation for slate recommendation. A multi-agent reinforcement learning model of common-pool resource appropriation. Balancing information exposure in social networks.

Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols. A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. Prabhat - Chris Pal. Gradient descent GAN optimization is locally stable. The Numerics of GANs. Learning to Pivot with Adversarial Networks. Gutmann - Charles Sutton. Dual Discriminator Generative Adversarial Nets. Triple Generative Adversarial Nets.

Triangle Generative Adversarial Networks. Structured Generative Adversarial Networks. Unsupervised Image-to-Image Translation Networks. Controllable Invariance through Adversarial Feature Learning.

Adversarial Ranking for Language Generation. Attention is All you Need. Masked Autoregressive Flow for Density Estimation. A simple neural network module for relational reasoning.

Train longer, generalize better: Robust multitask reinforcement learning. Learning Combinatorial Optimization Algorithms over Graphs. Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. Unsupervised Learning of Disentangled Representations from Video. Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach. Breaking the Nonsmooth Barrier: Approximate Supermodularity Bounds for Experimental Design.

Clustering with Noisy Queries. Approximation Algorithms for-Low Rank Approximation. Safe Adaptive Importance Sampling. Sharpness, Restart and Acceleration. Low-variance, unbiased gradient estimates for discrete latent variable models. Perturbative Black Box Variational Inference. Permutation-based Causal Inference Algorithms with Interventions. Conic Scan-and-Cover algorithms for nonparametric topic modeling. Reliable Decision Support using Counterfactual Models.

Unifying PAC and Regret: Repeated Inverse Reinforcement Learning. An algorithm for measuring the accuracy of probabilistic inference algorithms Marco Cusumano-Towner , Vikash K.

Warmuth Approximation Bounds for Hierarchical Clustering: Ihler Is the Bellman residual a bad proxy? Bui , Cuong Nguyen , Richard E. Salakhutdinov , Alexander J. A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events Evan Racah , Christopher Beckham , Tegan Maharaj , Samira Ebrahimi Kahou , Mr. Turaga Gaussian process based nonlinear latent structure discovery in multivariate spike train data Anqi Wu , Nicholas G.

Roy , Stephen Keeley , Jonathan W. Koh , Percy S. Chichilnisky , Gaute T. Archer , Lars Buesing , Srinivas C. Turaga , Jakob H. Hunt , Tom Schaul , Hado P. Mitchell , Eric J. Gorbach , Stefan Bauer , Joachim M.

Merel , Scott E. Boosting Generative Models Ilya O. Wainwright , Michael I. Jordan Learning from uncertain curves: Halloran , David M. Sharpnack , Ryan J. Tibshirani Training Quantized Nets: Jamieson , Martin J.