4 papers · 1 filter
Uncertainty-Aware Diagnostics for Physics-Informed Machine Learning
Mara Daniels, Liam Hodgkinson, Michael Mahoney
Physics-informed machine learning (PIML) integrates prior physical information, often in the form of differential equation constraints, into the process of fitting machine learning…
Spectral Estimation with Free Decompression
Siavash Ameli, Chris van der Heide, Liam Hodgkinson +1
Computing eigenvalues of very large matrices is a critical task in many machine learning applications, including the evaluation of log-determinants, the trace of matrix functions,…
Models of Heavy-Tailed Mechanistic Universality
Liam Hodgkinson, Zhichao Wang, Michael W. Mahoney
Recent theoretical and empirical successes in deep learning, including the celebrated neural scaling laws, are punctuated by the observation that many objects of interest tend to e…
Determinant Estimation under Memory Constraints and Neural Scaling Laws
Siavash Ameli, Chris van der Heide, Liam Hodgkinson +2
Calculating or accurately estimating log-determinants of large positive definite matrices is of fundamental importance in many machine learning tasks. While its cubic computational…