Papers
Factor Graph Grammars
David Chiang, Darcey Riley
Compositional Visual Generation with Energy Based Models
Yilun Du, Shuang Li, Igor Mordatch
Most ReLU Networks Suffer from ℓ^2 Adversarial Perturbations
Amit Daniely, Hadas Shacham
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
Zhe Gan, Yen-Chun Chen, Linjie Li et al.
Bootstrapping neural processes
Juho Lee, Yoonho Lee, Jungtaek Kim et al.
A Dictionary Approach to Domain-Invariant Learning in Deep Networks
Ze Wang, Xiuyuan Cheng, Guillermo Sapiro et al.
Towards Scalable Bayesian Learning of Causal DAGs
Jussi Viinikka, Antti Hyttinen, Johan Pensar et al.
Neural Power Units
Niklas Heim, Tomas Pevny, Vasek Smidl
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
Harm Van Seijen, Hadi Nekoei, Evan Racah et al.
Off-Policy Evaluation via the Regularized Lagrangian
Mengjiao Yang, Ofir Nachum, Bo Dai et al.
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes
Elena Smirnova, Elvis Dohmatob
Neutralizing Self-Selection Bias in Sampling for Sortition
Bailey Flanigan, Paul Gölz, Anupam Gupta et al.
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Florian Wenzel, Jasper Snoek, Dustin Tran et al.
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration
Kevin Scaman, Ludovic DOS SANTOS, Merwan Barlier et al.
Introducing Routing Uncertainty in Capsule Networks
Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias
SMYRF - Efficient Attention using Asymmetric Clustering
Giannis Daras, Nikita Kitaev, Augustus Odena et al.
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks
Alexander Levine, Soheil Feizi
Federated Principal Component Analysis
Andreas Grammenos, Rodrigo Mendoza Smith, Jon Crowcroft et al.
Benchmarking Deep Learning Interpretability in Time Series Predictions
Aya Abdelsalam Ismail, Mohamed Gunady, Hector Corrada Bravo et al.
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
Mengdi Xu, Wenhao Ding, Jiacheng Zhu et al.
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations
Rohan Paleja, Andrew Silva, Letian Chen et al.
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
Aman Sinha, Matthew O'Kelly, Russ Tedrake et al.
Detecting Interactions from Neural Networks via Topological Analysis
Zirui Liu, Qingquan Song, Kaixiong Zhou et al.
Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L Yamins et al.
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality
Kwang-Sung Jun, Chicheng Zhang