Kristen Morse — 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 ↗

2020

Learning State-Dependent Losses for Inverse Dynamics Learning

Kristen Morse, Neha Das, Yixin Lin, Austin S. Wang, Akshara Rai, Franziska Meier

IROS 2020poster

Being able to quickly adapt to changes in dynamics is paramount in model-based control for object manipulation tasks. In order to influence fast adaptation of the inverse dynamics model's parameters, data efficiency is crucial. Given observed data, a key element to how an optimizer updates model par…

Cited by 11 Source ↗Scholar ↗