26 citations · 72 across the 18 of their papers we have counts for
4 papers · 1 filter
A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks
Lior Deutsch, Erik Nijkamp, Yu Yang
Recent work on mode connectivity in the loss landscape of deep neural networks has demonstrated that the locus of (sub-)optimal weight vectors lies on continuous paths. In this wor…
Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)
Quanshi Zhang, Yu Yang, Ying Nian Wu
This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i.e., the explainer uses i…
Network Transplanting (extended abstract)
Quanshi Zhang, Yu Yang, Qian Yu +1
This paper focuses on a new task, i.e., transplanting a category-and-task-specific neural network to a generic, modular network without strong supervision. We design a functionally…
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…