Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Learning Dynamics of Deep Networks Admit Low-Rank Tensor Descriptions
Christopher H. Stock, Alex H. Williams, Madhu S. Advani, Andrew M. Saxe, Surya Ganguli
Feb 12, 2018 (modified: Jun 04, 2018)ICLR 2018 Workshop Submissionreaders: everyoneShow Bibtex
Abstract:Deep feedforward neural networks are associated with complicated, nonconvex objective functions. Yet, simple optimization algorithms can identify parameters that generalize well to held-out data. We currently lack detailed descriptions of this learning process, even on a qualitative level. We propose a simple tensor decomposition model to study how hidden representations evolve over learning. This approach precisely extracts the correct dynamics of learning in linear networks, which admit closed form solutions. On deep, nonlinear architectures performing image classification (CIFAR-10), we find empirically that a low-rank tensor model can explain a large fraction of variance while extracting meaningful features, such as stage-like learning and selectivity to inputs.
Keywords:Learning Dynamics, Deep Networks, Tensor Decomposition
TL;DR:We propose a simple unsupervised learning procedure based on tensor decomposition to concisely describe learning dynamics in deep networks.
Enter your feedback below and we'll get back to you as soon as possible.