37 citations · 127 across the 31 of their papers we have counts for
5 papers · 1 filter
Multimodal Foundation Models for Material Property Prediction and Discovery
Viggo Moro, Charlotte Loh, Rumen Dangovski +7
Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials. Recentl…
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies
Ligong Han, Seungwook Han, Shivchander Sudalairaj +8
Transformations based on domain expertise (expert transformations), such as random-resized-crop and color-jitter, have proven critical to the success of contrastive learning techni…
Model Stitching: Looking For Functional Similarity Between Representations
Adriano Hernandez, Rumen Dangovski, Peter Y. Lu +1
Model stitching (Lenc & Vedaldi 2015) is a compelling methodology to compare different neural network representations, because it allows us to measure to what degree they may be in…
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj +5
Deep ensembles (DE) have been successful in improving model performance by learning diverse members via the stochasticity of random initialization. While recent works have attempte…
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Owen Dugan, Peter Y. Lu, Rumen Dangovski +2
Studying the dynamics of open quantum systems can enable breakthroughs both in fundamental physics and applications to quantum engineering and quantum computation. Since the densit…