Berton Earnshaw — accepted AI-conference papers

2024

Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

Oren Kraus, Kian Kenyon-Dean, Saber Saberian, Maryam Fallah, Peter McLean, Jess Leung +11

CVPR 2024highlight

Featurizing microscopy images for use in biological research remains a significant challenge especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training wit…

Cited by 34 Source ↗PDF ↗code ↗Scholar ↗

2021

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani +17

ICML 2021oral

Distribution shifts—where the training distribution differs from the test distribution—can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity in the real-world deployments, these distribution shifts are under-represented in the datasets w…

Cited by 1715 Source ↗PDF ↗code ↗Scholar ↗