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cs.LG2024
Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism
Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu +2
An appropriate choice of batch sizes in large-scale model training is crucial, yet it involves an intrinsic yet inevitable dilemma: large-batch training improves training efficienc…
cs.LG2024
Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies
Yibo Wen, Chenwei Xu, Jerry Yao-Chieh Hu +2
We present a three-stage framework for training deep learning models specializing in antibody sequence-structure co-design. We first pre-train a language model using millions of an…
cs.LG2024
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods
Tim Tsz-Kit Lau, Han Liu, Mladen Kolar
The choice of batch size in minibatch stochastic gradient optimization is critical for both optimization and generalization performance in large-scale model training. Although larg…