9 citations · 20 across the 6 of their papers we have counts for
6 papers
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels
Nicholas Roberts, Xintong Li, Tzu-Heng Huang +7
Weak supervision (WS) is a powerful method to build labeled datasets for training supervised models in the face of little-to-no labeled data. It replaces hand-labeling data with ag…
Rotation-Equivariant Neural Networks for Privacy Protection
Hao Zhang, Yiting Chen, Haotian Ma +5
In order to prevent leaking input information from intermediate-layer features, this paper proposes a method to revise the traditional neural network into the rotation-equivariant…
Deep Quaternion Features for Privacy Protection
Hao Zhang, Yiting Chen, Liyao Xiang +3
We propose a method to revise the neural network to construct the quaternion-valued neural network (QNN), in order to prevent intermediate-layer features from leaking input informa…
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
Haotian Ma, Hao Zhang, Fan Zhou +2
This paper presents a method to explain how the information of each input variable is gradually discarded during the forward propagation in a deep neural network (DNN), which provi…
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
Zenan Ling, Haotian Ma, Yu Yang +3
In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of…
Interpretable Complex-Valued Neural Networks for Privacy Protection
Liyao Xiang, Haotian Ma, Hao Zhang +3
Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features. We study the possibility of preventing such ad…