Papers
11,015 papers found
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong et al.
Quantitatively Evaluating GANs With Divergences Proposed for Training
Daniel Jiwoong Im, He Ma, Graham W. Taylor et al.
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
Erin Grant, Chelsea Finn, Sergey Levine et al.
Regularizing and Optimizing LSTM Language Models
Stephen Merity, Nitish Shirish Keskar, Richard Socher
Reinforcement Learning Algorithm Selection
Romain Laroche, Raphael Feraud
Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration
Evan Zheran Liu, Kelvin Guu, Panupong Pasupat et al.
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
Sjoerd van Steenkiste, Michael Chang, Klaus Greff et al.
Residual Connections Encourage Iterative Inference
Stanisław Jastrzebski, Devansh Arpit, Nicolas Ballas et al.
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback
Hal Daumé III, John Langford, Amr Sharaf
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
Jianbo Ye, Xin Lu, Zhe Lin et al.
Robustness of Classifiers to Universal Perturbations: A Geometric Perspective
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi et al.
Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning
Clemens Rosenbaum, Tim Klinger, Matthew Riemer
Scalable Private Learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov et al.
SCAN: Learning Hierarchical Compositional Visual Concepts
Irina Higgins, Nicolas Sonnerat, Loic Matthey et al.
SEARNN: Training RNNs with global-local losses
Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin et al.
Self-ensembling for visual domain adaptation
Geoff French, Michal Mackiewicz, Mark Fisher
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
Chris Donahue, Zachary C. Lipton, Akshay Balsubramani et al.
Semantic Interpolation in Implicit Models
Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann
Semi-parametric topological memory for navigation
Nikolay Savinov, Alexey Dosovitskiy, Vladlen Koltun
Sensitivity and Generalization in Neural Networks: an Empirical Study
Roman Novak, Yasaman Bahri, Daniel A. Abolafia et al.
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data
Alon Brutzkus, Amir Globerson, Eran Malach et al.
Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings
Kangwook Lee, Hoon Kim, Changho Suh
Simulating Action Dynamics with Neural Process Networks
Antoine Bosselut, Omer Levy, Ari Holtzman et al.
Skip Connections Eliminate Singularities
Emin Orhan, Xaq Pitkow
Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks
Víctor Campos, Brendan Jou, Xavier Giró-i-Nieto et al.