Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

1 code implementation • 16 Apr 2024 • Oren Kraus, Kian Kenyon-Dean, Saber Saberian, Maryam Fallah, Peter McLean, Jess Leung, Vasudev Sharma, Ayla Khan, Jia Balakrishnan, Safiye Celik, Dominique Beaini, Maciej Sypetkowski, Chi Vicky Cheng, Kristen Morse, Maureen Makes, Ben Mabey, Berton Earnshaw

Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images.

Drug Discovery Self-Supervised Learning

Masked Autoencoders are Scalable Learners of Cellular Morphology

1 code implementation • 27 Sep 2023 • Oren Kraus, Kian Kenyon-Dean, Saber Saberian, Maryam Fallah, Peter McLean, Jess Leung, Vasudev Sharma, Ayla Khan, Jia Balakrishnan, Safiye Celik, Maciej Sypetkowski, Chi Vicky Cheng, Kristen Morse, Maureen Makes, Ben Mabey, Berton Earnshaw

Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research.

Learning State-Dependent Losses for Inverse Dynamics Learning

1 code implementation • 10 Mar 2020 • Kristen Morse, Neha Das, Yixin Lin, Austin S. Wang, Akshara Rai, Franziska Meier

In both settings, the structured and state-dependent learned losses improve online adaptation speed, when compared to standard, state-independent loss functions.

Meta-Learning