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Explicitising The Implicit Intrepretability of Deep Neural Networks Via Duality
[article]
2022
arXiv
pre-print
Recent work by Lakshminarayanan and Singh [2020] provided a dual view for fully connected deep neural networks (DNNs) with rectified linear units (ReLU). It was shown that (i) the information in the gates is analytically characterised by a kernel called the neural path kernel (NPK) and (ii) most critical information is learnt in the gates, in that, given the learnt gates, the weights can be retrained from scratch without significant loss in performance. Using the dual view, in this paper, we
arXiv:2203.16455v1
fatcat:sevasqw67jfcpifb2pzlgz3w3i