8 citations · 13 across the 6 of their papers we have counts for
7 papers
Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit
Junqi Yin, Mijanur Palash, M. Paul Laiu +6
Turbulence plays a crucial role in multiphysics applications, including aerodynamics, fusion, and combustion. Accurately capturing turbulence's multiscale characteristics is essent…
Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
Payel Bhattacharjee, Fengwei Tian, Geoffrey D. Rubin +5
Large Language Models (LLMs) are increasingly adopted across domains such as education, healthcare, and finance. In healthcare, LLMs support tasks including disease diagnosis, abno…
MATEY: multiscale adaptive foundation models for spatiotemporal physical systems
Pei Zhang, M. Paul Laiu, Matthew Norman +2
Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer (ViT) architectures requires extremely long, computationally prohibit…
Adiabatic Quantum Support Vector Machines
Prasanna Date, Dong Jun Woun, Kathleen Hamilton +7
Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine le…
Ultra-Long Sequence Distributed Transformer
Xiao Wang, Isaac Lyngaas, Aristeidis Tsaris +7
Transformer models trained on long sequences often achieve higher accuracy than short sequences. Unfortunately, conventional transformers struggle with long sequence training due t…
Transferring a molecular foundation model for polymer property predictions
Pei Zhang, Logan Kearney, Debsindhu Bhowmik +3
Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and materials discovery. Self-supervis…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…