41 citations · 69 across the 9 of their papers we have counts for
9 papers
The Local Learning Coefficient: A Singularity-Aware Complexity Measure
Edmund Lau, Zach Furman, George Wang +2
The Local Learning Coefficient (LLC) is introduced as a novel complexity measure for deep neural networks (DNNs). Recognizing the limitations of traditional complexity measures, th…
Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset
Hui Li, Carlos A. Pena Solorzano, Susan Wei +1
The ADMANI datasets (annotated digital mammograms and associated non-image datasets) from the Transforming Breast Cancer Screening with AI programme (BRAIx) run by BreastScreen Vic…
Variational Bayesian Neural Networks via Resolution of Singularities
Susan Wei, Edmund Lau
In this work, we advocate for the importance of singular learning theory (SLT) as it pertains to the theory and practice of variational inference in Bayesian neural networks (BNNs)…
Counterfactual Fairness with Partially Known Causal Graph
Aoqi Zuo, Susan Wei, Tongliang Liu +3
Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair mach…
Deep Learning is Singular, and That's Good
Daniel Murfet, Susan Wei, Mingming Gong +3
In singular models, the optimal set of parameters forms an analytic set with singularities and classical statistical inference cannot be applied to such models. This is significant…
The Fairness-Accuracy Pareto Front
Susan Wei, Marc Niethammer
Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy. We provide…