1 citations · 1 across the 7 of their papers we have counts for
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Absorbing State Phase Transitions in Multi-Agent Search
Wenwen Zheng, Yuzhe Yang, Helen Qu +2
Nontrivial dynamics can emerge in large language model (LLM)-based multi-agent systems, and preliminary evidence exists that formalisms from statistical mechanics can be effective…
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20
Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Ray…
MIMIC: A Generative Multimodal Foundation Model for Biomolecules
Siavash Golkar, Jake Kovalic, Irina Espejo Morales +28
Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models in biology are trained with…
Representation Learning for Spatiotemporal Physical Systems
Helen Qu, Rudy Morel, Michael McCabe +4
Machine learning approaches to spatiotemporal physical systems have primarily focused on next-frame prediction, with the goal of learning an accurate emulator for the system's evol…
Measuring the Vertical Structure of Active Galactic Nuclei Disks with Transformer Models and the Vera C. Rubin Observatory
Amy Secunda, Sebastian Wagner-Carena, Helen Qu +1
Reverberation mapping is one of the main techniques used to study active galactic nuclei (AGN) accretion disks. Traditional continuum reverberation mapping uses short lags between…