32 citations · 52 across the 20 of their papers we have counts for
24 papers
Why Heavy-Tailed Weight Spectra Predict Model Quality
Joseph Wilson, Chris van der Heide, Liam Hodgkinson +3
A solid understanding of the predictive success of deep neural networks (DNNs) remains elusive. For example, while many data-dependent metrics exist that seek to predict model qual…
Benign Loss Landscapes Can Coexist with Worst-Case Hardness
Zach Furman, Stephan Wäldchen, Yangda Bei +1
Deep neural networks are expressive enough to contain worst-case targets that can be evaluated in polynomial time but cannot be learned in polynomial time by gradient descent. For…
SCAN: Sequentially Detecting Change-points via Adaptive Nonparametric Inference
Ashoka Prabashwara, Patricia Menéndez, Liam Hodgkinson +1
Modern time series are often long, serially dependent, and non-stationary. Existing change-point methods either target specific changes or become computationally intensive when usi…
Is the Last Layer Sufficient for Uncertainty Quantification?
Joseph Wilson, Chris van der Heide, Liam Hodgkinson +1
Epistemic uncertainty quantification (UQ) for deep neural networks (DNNs) is a requirement for safe adoption of AI in mission-critical settings. Several leading methods for UQ line…
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…
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…