8 citations · 30 across the 15 of their papers we have counts for
15 papers
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning
Hongwei Jin, George Papadimitriou, Krishnan Raghavan +5
Anomaly detection in computational workflows is critical for ensuring system reliability and security. However, traditional rule-based methods struggle to detect novel anomalies. T…
Network architecture search of X-ray based scientific applications
Adarsha Balaji, Ramyad Hadidi, Gregory Kollmer +2
X-ray and electron diffraction-based microscopy use bragg peak detection and ptychography to perform 3-D imaging at an atomic resolution. Typically, these techniques are implemente…
AI Competitions and Benchmarks: Dataset Development
Romain Egele, Julio C. S. Jacques Junior, Jan N. van Rijn +7
Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting…
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
Yixuan Sun, Ololade Sowunmi, Romain Egele +3
Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage the advanced search algorithms for multiobjectiv…
Transfer-Learning-Based Autotuning Using Gaussian Copula
Thomas Randall, Jaehoon Koo, Brice Videau +6
As diverse high-performance computing (HPC) systems are built, many opportunities arise for applications to solve larger problems than ever before. Given the significantly increase…
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…