44 citations · 46 across the 6 of their papers we have counts for
8 papers
Free Decompression with Algebraic Spectral Curves
Siavash Ameli, Chris van der Heide, Liam Hodgkinson +1
Tools from random matrix theory have become central to deep learning theory, using spectral information to provide mechanisms for modeling generalization, robustness, scaling, and…
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…
Uncertainty Quantification with the Empirical Neural Tangent Kernel
Joseph Wilson, Chris van der Heide, Liam Hodgkinson +1
While neural networks have demonstrated impressive performance across various tasks, accurately quantifying uncertainty in their predictions is essential to ensure their trustworth…
Temperature Optimization for Bayesian Deep Learning
Kenyon Ng, Chris van der Heide, Liam Hodgkinson +1
The Cold Posterior Effect (CPE) is a phenomenon in Bayesian Deep Learning (BDL), where tempering the posterior to a cold temperature often improves the predictive performance of th…
The Interpolating Information Criterion for Overparameterized Models
Liam Hodgkinson, Chris van der Heide, Robert Salomone +2
The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset. Classical informatio…
Stochastic Normalizing Flows
Liam Hodgkinson, Chris van der Heide, Fred Roosta +1
We introduce stochastic normalizing flows, an extension of continuous normalizing flows for maximum likelihood estimation and variational inference (VI) using stochastic differenti…