52 citations · 150 across the 24 of their papers we have counts for
4 papers · 1 filter
Linear Time Kernel Matrix Approximation via Hyperspherical Harmonics
John Paul Ryan, Anil Damle
We propose a new technique for constructing low-rank approximations of matrices that arise in kernel methods for machine learning. Our approach pairs a novel automatically construc…
Model Preserving Compression for Neural Networks
Jerry Chee, Megan Renz, Anil Damle +1
After training complex deep learning models, a common task is to compress the model to reduce compute and storage demands. When compressing, it is desirable to preserve the origina…
The Fast Kernel Transform
John Paul Ryan, Sebastian Ament, Carla P. Gomes +1
Kernel methods are a highly effective and widely used collection of modern machine learning algorithms. A fundamental limitation of virtually all such methods are computations invo…
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss, Martin Jankowiak, David Eriksson +2
Matrix square roots and their inverses arise frequently in machine learning, e.g., when sampling from high-dimensional Gaussians or whitening a…