Learning Compressed Transforms With Low Displacement Rank

Authors:
Anna Thomas Stanford
Albert Gu Stanford
Tri Dao Stanford University
Atri Rudra University at Buffalo, SUNY
Chris Ré Stanford

Introduction:

The low displacement rank (LDR) framework for structured matrices represents a matrix through two displacement operators and a low-rank residual.

Abstract:

The low displacement rank (LDR) framework for structured matrices represents a matrix through two displacement operators and a low-rank residual. Existing use of LDR matrices in deep learning has applied fixed displacement operators encoding forms of shift invariance akin to convolutions. We introduce a rich class of LDR matrices with more general displacement operators, and explicitly learn over both the operators and the low-rank component. This class generalizes several previous constructions while preserving compression and efficient computation. We prove bounds on the VC dimension of multi-layer neural networks with structured weight matrices and show empirically that our compact parameterization can reduce the sample complexity of learning. When replacing weight layers in fully-connected, convolutional, and recurrent neural networks for image classification and language modeling tasks, our new classes exceed the accuracy of existing compression approaches, and on some tasks even outperform general unstructured layers while using more than 20x fewer parameters.

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