786 citations · 3k across the 61 of their papers we have counts for
6 papers · 2 filters
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Stephen H. Bach, Daniel Rodriguez, Yintao Liu +10
Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications. We present a first-of-its-kind study showing how existing knowledge…
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…
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…
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
Cody Coleman, Daniel Kang, Deepak Narayanan +7
Researchers have proposed hardware, software, and algorithmic optimizations to improve the computational performance of deep learning. While some of these optimizations perform the…
High-Accuracy Low-Precision Training
Christopher De Sa, Megan Leszczynski, Jian Zhang +4
Low-precision computation is often used to lower the time and energy cost of machine learning, and recently hardware accelerators have been developed to support it. Still, it has b…
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…