193 citations · 457 across the 21 of their papers we have counts for
4 papers · 1 filter
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning
Zhengwei Fang, Rui Wang, Tao Huang +1
Strong adversarial examples are crucial for evaluating and enhancing the robustness of deep neural networks. However, the performance of popular attacks is usually sensitive, for i…
Relational Surrogate Loss Learning
Tao Huang, Zekang Li, Hua Lu +6
Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e.g., average precision and F1 score. Thi…
Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning
Tao Huang, Jiachen Wang, Xiao Chen
Learning informative representations from image-based observations is of fundamental concern in deep Reinforcement Learning (RL). However, data-inefficiency remains a significant b…
MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks
Tao Huang, Yihan Zhang, Jiajing Wu +2
Graph convolutional networks (GCNs) have been employed as a kind of significant tool on many graph-based applications recently. Inspired by convolutional neural networks (CNNs), GC…