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Discovering and Explaining the Representation Bottleneck of DNNs
This paper explores the bottleneck of feature representations of deep neural networks (DNNs), from the perspective of the complexity of …
Huiqi Deng, Qihan Ren, Xu Chen, Hao Zhang, Jie Ren, Quanshi Zhang
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A General Taylor Framework for Unifying and Revisiting Attribution Methods
Attribution methods provide an insight into the decision-making process of machine learning models, especially deep neural networks, by …
Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Xia Hu
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Mutual Information Preserving Back-propagation Learn to Invert for Faithful Attribution
Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, Xia Hu
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Invariant subspace learning for time series data based on dynamic time warping distance
Low-dimensional and compact representation of time series data is of importance for mining and storage. In practice, time series data …
Huiqi Deng, Weifu Chen, Qi Shen, Andy Jinhua Ma, PongChi Yuen, Guocan Feng
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