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CoLA: Exploiting Compositional Structure for Automatic and Efficient
  Numerical Linear Algebra
v1v2 (latest)

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra

Neural Information Processing Systems (NeurIPS), 2023
6 September 2023
Andres Potapczynski
Marc Finzi
Geoff Pleiss
Andrew Gordon Wilson
ArXiv (abs)PDFHTML

Papers citing "CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra"

8 / 8 papers shown
Title
Hidden Breakthroughs in Language Model Training
Hidden Breakthroughs in Language Model Training
Sara Kangaslahti
Elan Rosenfeld
Naomi Saphra
179
5
0
18 Jun 2025
Spectral Estimation with Free Decompression
Spectral Estimation with Free Decompression
Siavash Ameli
Chris van der Heide
Liam Hodgkinson
Michael W. Mahoney
141
0
0
13 Jun 2025
Compute-Optimal LLMs Provably Generalize Better With Scale
Compute-Optimal LLMs Provably Generalize Better With ScaleInternational Conference on Learning Representations (ICLR), 2025
Marc Finzi
Sanyam Kapoor
Diego Granziol
Anming Gu
Christopher De Sa
J. Zico Kolter
Andrew Gordon Wilson
348
4
0
21 Apr 2025
Diffusing Differentiable Representations
Diffusing Differentiable RepresentationsNeural Information Processing Systems (NeurIPS), 2024
Yash Savani
Marc Finzi
J. Zico Kolter
DiffM
176
0
0
09 Dec 2024
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects ModelsNeural Information Processing Systems (NeurIPS), 2024
Jinlin Lai
Justin Domke
Daniel Sheldon
347
0
0
31 Oct 2024
Searching for Efficient Linear Layers over a Continuous Space of
  Structured Matrices
Searching for Efficient Linear Layers over a Continuous Space of Structured MatricesNeural Information Processing Systems (NeurIPS), 2024
Andres Potapczynski
Shikai Qiu
Marc Finzi
Christopher Ferri
Zixi Chen
Micah Goldblum
Bayan Bruss
Christopher De Sa
Andrew Gordon Wilson
185
8
0
03 Oct 2024
Perspectives on the State and Future of Deep Learning - 2023
Perspectives on the State and Future of Deep Learning - 2023
Micah Goldblum
A. Anandkumar
Richard Baraniuk
Tom Goldstein
Kyunghyun Cho
Zachary Chase Lipton
Melanie Mitchell
Preetum Nakkiran
Max Welling
Andrew Gordon Wilson
343
5
0
07 Dec 2023
Any-dimensional equivariant neural networks
Any-dimensional equivariant neural networksInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Eitan Levin
Mateo Díaz
170
9
0
10 Jun 2023
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