8 citations · 8 across the 1 of their papers we have counts for
3 papers
cs.LG2021★ 8 cited
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps
Tri Dao, Nimit S. Sohoni, Albert Gu +5
Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference…
cs.LG2019
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
Tri Dao, Albert Gu, Matthew Eichhorn +2
Fast linear transforms are ubiquitous in machine learning, including the discrete Fourier transform, discrete cosine transform, and other structured transformations such as convolu…
stat.ML2018
Fast Counting in Machine Learning Applications
Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt +2
We propose scalable methods to execute counting queries in machine learning applications. To achieve memory and computational efficiency, we abstract counting queries and their con…