3 citations · 4 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
A Differential Topological View of Challenges in Learning with Feedforward Neural Networks
Hao Shen
Among many unsolved puzzles in theories of Deep Neural Networks (DNNs), there are three most fundamental challenges that highly demand solutions, namely, expressibility, optimisabi…