Papers
On Testing of Samplers
Kuldeep S Meel, Yash Pralhad Pote, Sourav Chakraborty
Bayesian Bits: Unifying Quantization and Pruning
Mart van Baalen, Christos Louizos, Markus Nagel et al.
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Yue Wu, Pan Zhou, Andrew G Wilson et al.
SLIP: Learning to predict in unknown dynamical systems with long-term memory
Paria Rashidinejad, Jiantao Jiao, Stuart J. Russell
Meta-Learning Requires Meta-Augmentation
Janarthanan Rajendran, Alexander Irpan, Eric Jang
Gradient Estimation with Stochastic Softmax Tricks
Max Paulus, Dami Choi, Daniel Tarlow et al.
Self-supervised Co-Training for Video Representation Learning
Tengda Han, Weidi Xie, Andrew Zisserman
A Catalyst Framework for Minimax Optimization
Junchi Yang, Siqi Zhang, Negar Kiyavash et al.
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views
Nanbo Li, Cian Eastwood, Robert Fisher
Entrywise convergence of iterative methods for eigenproblems
Vasileios Charisopoulos, Austin R Benson, Anil Damle
Unfolding the Alternating Optimization for Blind Super Resolution
zhengxiong luo, Yan Huang, Shang Li et al.
RepPoints v2: Verification Meets Regression for Object Detection
Yihong Chen, Zheng Zhang, Yue Cao et al.
Learning of Discrete Graphical Models with Neural Networks
Abhijith Jayakumar, Andrey Lokhov, Sidhant Misra et al.
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
Chongli Qin, Yan Wu, Jost Tobias Springenberg et al.
Policy Improvement via Imitation of Multiple Oracles
Ching-An Cheng, Andrey Kolobov, Alekh Agarwal
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks
Jingqiu Ding, Samuel Hopkins, David Steurer
Latent World Models For Intrinsically Motivated Exploration
Aleksandr Ermolov, Nicu Sebe
Structured Convolutions for Efficient Neural Network Design
Yash Bhalgat, Yizhe Zhang, Jamie Menjay Lin et al.
The Value Equivalence Principle for Model-Based Reinforcement Learning
Christopher Grimm, Andre Barreto, Satinder P. Singh et al.
Independent Policy Gradient Methods for Competitive Reinforcement Learning
Constantinos Daskalakis, Dylan J Foster, Noah Golowich
Towards practical differentially private causal graph discovery
Lun Wang, Qi Pang, Dawn Song
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Huanrui Yang, Jingyang Zhang, Hongliang Dong et al.
Language Through a Prism: A Spectral Approach for Multiscale Language Representations
Alex Tamkin, Dan Jurafsky, Noah Goodman
Sub-sampling for Efficient Non-Parametric Bandit Exploration
Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard