Search Results for author: Iain Carmichael

Found 5 papers, 4 papers with code

Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling

1 code implementation17 Jun 2022 Iain Carmichael, Andrew H. Song, Richard J. Chen, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood

Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling complex features of the tumor microenvironment.

Survival Prediction whole slide images

An exposition of the false confidence theorem

1 code implementation17 Jul 2018 Iain Carmichael, Jonathan P Williams

A recent paper presents the "false confidence theorem" (FCT) which has potentially broad implications for statistical inference using Bayesian posterior uncertainty.

Methodology

Geometric Insights into Support Vector Machine Behavior using the KKT Conditions

1 code implementation3 Apr 2017 Iain Carmichael, J. S. Marron

The support vector machine (SVM) is a powerful and widely used classification algorithm.

mvlearn: Multiview Machine Learning in Python

no code implementations25 May 2020 Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein

As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have ballooned in recent years.

BIG-bench Machine Learning

Measure of Strength of Evidence for Visually Observed Differences between Subpopulations

1 code implementation2 Jan 2021 Xi Yang, Jan Hannig, Katherine A. Hoadley, Iain Carmichael, J. S. Marron

For measuring the strength of visually-observed subpopulation differences, the Population Difference Criterion is proposed to assess the statistical significance of visually observed subpopulation differences.

Cannot find the paper you are looking for? You can Submit a new open access paper.