1 code implementation • NeurIPS 2023 • Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson
In this paper, we propose a simple but general framework for large-scale linear algebra problems in machine learning, named CoLA (Compositional Linear Algebra).
1 code implementation • 19 Jun 2023 • Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson
A major challenge to out-of-distribution generalization is reliance on spurious features -- patterns that are predictive of the class label in the training data distribution, but not causally related to the target.
1 code implementation • 28 Apr 2023 • Marc Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson
Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical solvers is difficult or impossible.
1 code implementation • 24 Nov 2022 • Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson
While there has been progress in developing non-vacuous generalization bounds for deep neural networks, these bounds tend to be uninformative about why deep learning works.
1 code implementation • 14 Jul 2022 • Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson
Low-precision arithmetic has had a transformative effect on the training of neural networks, reducing computation, memory and energy requirements.
2 code implementations • 28 Apr 2022 • Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Andres Potapczynski, John P. Cunningham
This family enjoys remarkable mathematical simplicity; its density function resembles that of the Dirichlet distribution, but with a normalizing constant that can be written in closed form using elementary functions only.
1 code implementation • 12 Feb 2021 • Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham
In the case of RFF, we show that the bias-to-variance conversion is indeed a trade-off: the additional variance proves detrimental to optimization.
1 code implementation • NeurIPS 2020 • Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
The Gumbel-Softmax is a continuous distribution over the simplex that is often used as a relaxation of discrete distributions.