1 code implementation • 8 Dec 2023 • Teddy Koker, Keegan Quigley, Eric Taw, Kevin Tibbetts, Lin Li
The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing challenge.
no code implementations • 30 Oct 2023 • Keegan Quigley, Miriam Cha, Josh Barua, Geeticka Chauhan, Seth Berkowitz, Steven Horng, Polina Golland
Vision-language pretraining has been shown to produce high-quality visual encoders which transfer efficiently to downstream computer vision tasks.
no code implementations • 24 Nov 2022 • Teddy Koker, Keegan Quigley, Will Spaeth, Nathan C. Frey, Lin Li
By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks.
no code implementations • 5 Aug 2022 • Keegan Quigley, Miriam Cha, Ruizhi Liao, Geeticka Chauhan, Steven Horng, Seth Berkowitz, Polina Golland
In this paper, we build a data-efficient learning framework that utilizes radiology reports to improve medical image classification performance with limited labeled data (fewer than 1000 examples).
1 code implementation • 8 Mar 2021 • Ruizhi Liao, Daniel Moyer, Miriam Cha, Keegan Quigley, Seth Berkowitz, Steven Horng, Polina Golland, William M. Wells
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.