463 citations · 1.5k across the 29 of their papers we have counts for
Showing 2018Show all
3 papers · 1 filter
cs.LG2018★ 4 cited
Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation
Jian Zhang, Avner May, Tri Dao +1
We investigate how to train kernel approximation methods that generalize well under a memory budget. Building on recent theoretical work, we define a measure of kernel approximatio…
cs.LG2018★ 3 cited
Learning Compressed Transforms with Low Displacement Rank
Anna T. Thomas, Albert Gu, Tri Dao +2
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 de…
cs.LG2018★ 99 cited
A Kernel Theory of Modern Data Augmentation
Tri Dao, Albert Gu, Alexander J. Ratner +3
Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek…