7 citations · 12 across the 6 of their papers we have counts for
6 papers
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks
Marc Finzi, Andres Potapczynski, Matthew Choptuik +1
Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providin…
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization
Sanae Lotfi, Marc Finzi, Sanyam Kapoor +3
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. In this…
Low-Precision Arithmetic for Fast Gaussian Processes
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. However, despite its promise, low…
On the Normalizing Constant of the Continuous Categorical Distribution
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Andres Potapczynski +1
Probability distributions supported on the simplex enjoy a wide range of applications across statistics and machine learning. Recently, a novel family of such distributions has bee…
Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski, Luhuan Wu, Dan Biderman +2
Scalable Gaussian Process methods are computationally attractive, yet introduce modeling biases that require rigorous study. This paper analyzes two common techniques: early trunca…
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
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. Because it can be readily interpreted and easily repa…