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Towards interpreting deep neural networks via layer behavior understanding

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Publication:2673336
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DOI10.1007/s10994-021-06074-8zbMath1491.68178OpenAlexW4220673036WikidataQ114955313 ScholiaQ114955313MaRDI QIDQ2673336

Mingkui Tan, Xiping Hu, Xiangmiao Wu, Jiezhang Cao, Jincheng Li

Publication date: 9 June 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-021-06074-8

zbMATH Keywords

Wasserstein distancelayer behaviorteacher-student paradigm


Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Optimal transportation (49Q22)



Uses Software

  • word2vec
  • ImageNet
  • CNN-RNN
  • SVCCA
  • PASCAL VOC
  • BigGAN


Cites Work

  • Unnamed Item
  • Unnamed Item
  • Computational Optimal Transport: With Applications to Data Science
  • Gradient descent optimizes over-parameterized deep ReLU networks
  • Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach
  • Spanning attack: reinforce black-box attacks with unlabeled data
  • The Sinkhorn–Knopp Algorithm: Convergence and Applications
  • On the information bottleneck theory of deep learning
  • Dynamics of stochastic gradient descent for two-layer neural networks in the teacher–student setup*
  • Optimal Transport
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