20 citations · 68 across the 17 of their papers we have counts for
5 papers · 1 filter
Trap of Feature Diversity in the Learning of MLPs
Dongrui Liu, Shaobo Wang, Jie Ren +4
In this paper, we focus on a typical two-phase phenomenon in the learning of multi-layer perceptrons (MLPs), and we aim to explain the reason for the decrease of feature diversity…
Discovering and Explaining the Representation Bottleneck of DNNs
Huiqi Deng, Qihan Ren, Hao Zhang +1
This paper explores the bottleneck of feature representations of deep neural networks (DNNs), from the perspective of the complexity of interactions between input variables encoded…
Defining and Quantifying the Emergence of Sparse Concepts in DNNs
Jie Ren, Mingjie Li, Qirui Chen +2
This paper aims to illustrate the concept-emerging phenomenon in a trained DNN. Specifically, we find that the inference score of a DNN can be disentangled into the effects of a fe…
A General Taylor Framework for Unifying and Revisiting Attribution Methods
Huiqi Deng, Na Zou, Mengnan Du +3
Attribution methods provide an insight into the decision-making process of machine learning models, especially deep neural networks, by assigning contribution scores to each indivi…
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution
Huiqi Deng, Na Zou, Weifu Chen +3
Back propagation based visualizations have been proposed to interpret deep neural networks (DNNs), some of which produce interpretations with good visual quality. However, there ex…