Alan Moses — accepted AI-conference papers

2026

Predicting evolutionary rate as a pretraining task improves genome language model representations

Mica Consens, Kevin Yang, James Hall, Ashley Conard, BO WANG, Lorin Crawford +2

ICML 2026poster

Genome language models (gLM) have the potential to further understanding of regulatory genomics without requiring labeled data. Most gLMs are pretrained using sequence reconstruction tasks inspired by natural language processing, but recent studies have shown that these gLMs often fail to capture bi…

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2019

The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers

Alex Lu, Amy Lu, Wiebke Schormann, Marzyeh Ghassemi, David Andrews, Alan Moses

NeurIPS 2019poster

Understanding if classifiers generalize to out-of-sample datasets is a central problem in machine learning. Microscopy images provide a standardized way to measure the generalization capacity of image classifiers, as we can image the same classes of objects under increasingly divergent, but controll…

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