2.2k citations · 10.8k across the 93 of their papers we have counts for
16 papers · 1 filter
How Should an Agent Practice?
Janarthanan Rajendran, Richard Lewis, Vivek Veeriah +2
We present a method for learning intrinsic reward functions to drive the learning of an agent during periods of practice in which extrinsic task rewards are not available. During p…
Efficient Adversarial Training with Transferable Adversarial Examples
Haizhong Zheng, Ziqi Zhang, Juncheng Gu +2
Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require or…
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks
Ruben Villegas, Arkanath Pathak, Harini Kannan +3
Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time. Previously proposed sol…
Consistency Regularization for Generative Adversarial Networks
Han Zhang, Zizhao Zhang, Augustus Odena +1
Generative Adversarial Networks (GANs) are known to be difficult to train, despite considerable research effort. Several regularization techniques for stabilizing training have bee…
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
Kimin Lee, Kibok Lee, Jinwoo Shin +1
Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dim…
Distilling Effective Supervision from Severe Label Noise
Zizhao Zhang, Han Zhang, Sercan O. Arik +2
Collecting large-scale data with clean labels for supervised training of neural networks is practically challenging. Although noisy labels are usually cheap to acquire, existing me…
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Ofir Nachum, Haoran Tang, Xingyu Lu +3
Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks. Previous works have motivated the use of hierarchy…
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards
Yijie Guo, Jongwook Choi, Marcin Moczulski +4
Reinforcement learning with sparse rewards is challenging because an agent can rarely obtain non-zero rewards and hence, gradient-based optimization of parameterized policies can b…
Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks
Xinchen Yan, Mohi Khansari, Jasmine Hsu +4
Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data. To work well in…
Zero-Shot Entity Linking by Reading Entity Descriptions
Lajanugen Logeswaran, Ming-Wei Chang, Kenton Lee +3
We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data. The goal is to enable robust transfer to highly speci…
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
Haonan Qiu, Chaowei Xiao, Lei Yang +3
Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to…
Unsupervised Learning of Object Structure and Dynamics from Videos
Matthias Minderer, Chen Sun, Ruben Villegas +3
Extracting and predicting object structure and dynamics from videos without supervision is a major challenge in machine learning. To address this challenge, we adopt a keypoint-bas…
Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee +1
Recent work has sought to understand the behavior of neural networks by comparing representations between layers and between different trained models. We examine methods for compar…
Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild
Kibok Lee, Kimin Lee, Jinwoo Shin +1
Lifelong learning with deep neural networks is well-known to suffer from catastrophic forgetting: the performance on previous tasks drastically degrades when learning a new task. T…
Diversity-Sensitive Conditional Generative Adversarial Networks
Dingdong Yang, Seunghoon Hong, Yunseok Jang +2
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distribution…
Robust Inference via Generative Classifiers for Handling Noisy Labels
Kimin Lee, Sukmin Yun, Kibok Lee +3
Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such…