3 citations · 3 across the 1 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2025★ 3 cited
Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
Jaesun Kim, Jinmu You, Yutack Park +11
Accurate yet transferable machine-learning interatomic potentials (MLIPs) are essential for accelerating materials and chemical discovery. However, most universal MLIPs overfit to…
cs.LG2024
vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training
Jehyeon Bang, Yujeong Choi, Myeongwoo Kim +2
As large language models (LLMs) become widespread in various application domains, a critical challenge the AI community is facing is how to train these large AI models in a cost-ef…
cs.LG2024
Breaking MLPerf Training: A Case Study on Optimizing BERT
Yongdeok Kim, Jaehyung Ahn, Myeongwoo Kim +11
Speeding up the large-scale distributed training is challenging in that it requires improving various components of training including load balancing, communication, optimizers, et…