Country
Gaussian-Based Pooling for Convolutional Neural Networks
In recent years, convolutional neural networks (CNNs) are applied to various visual recognition tasks with great success [7, 8, 14].
Thwarting Adversarial Examples: An $L_0$-Robust Sparse Fourier Transform
Mitali Bafna, Jack Murtagh, Nikhil Vyas
In this work, we give a framework for improving the robustness of classifiers to adversaries with L0 noise budgets.
Learning to Multitask
Yu Zhang, Ying Wei, Qiang Yang
Neural Information Processing Systems http://nips.cc/
Divergence-Augmented Policy Optimization
Qing Wang, Yingru Li, Jiechao Xiong, Tong Zhang
In deep reinforcement learning, policy optimization methods need to deal with issues such asfunction approximation andthereuse ofoff-policydata.
Game Design for Eliciting Distinguishable Behavior
Fan Yang, Liu Leqi, Yifan Wu, Zachary Lipton, Pradeep K. Ravikumar, Tom M. Mitchell, William W. Cohen
However, these traditional games are limited because they are typically designed based on heuristics.
Masking: A New Perspective of Noisy Supervision
Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, Masashi Sugiyama
Thus, by estimating this matrix, classifiers can escape from overfitting those noisy labels.
Diffusion Policies Creating a Trust Region for Offline Reinforcement Learning
Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressive policy class, significantly boosts the performance of offline RL. However, its reliance on iterative denoising sampling to generate actions slows down both training and inference.