5 citations · 13 across the 8 of their papers we have counts for
8 papers
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification
Jianzhang Zheng, Fan Yang, Hao Shen +4
Adversarial attacks are often considered as threats to the robustness of Deep Neural Networks (DNNs). Various defending techniques have been developed to mitigate the potential neg…
Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrations
Hao Shen, Weikang Wan, He Wang
Generalizable object manipulation skills are critical for intelligent and multi-functional robots to work in real-world complex scenes. Despite the recent progress in reinforcement…
Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning
Jinxin Liu, Hao Shen, Donglin Wang +2
Unsupervised reinforcement learning aims to acquire skills without prior goal representations, where an agent automatically explores an open-ended environment to represent goals an…
Adversarial Learning with Cost-Sensitive Classes
Haojing Shen, Sihong Chen, Ran Wang +1
It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining c…
Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning
Alexander Sagel, Amit Sahu, Stefan Matthes +5
Research in machine learning is at a turning point. While supervised deep learning has conquered the field at a breathtaking pace and demonstrated the ability to solve inference pr…
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks
Haojing Shen, Sihong Chen, Ran Wang
This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max propert…