Research Area:  Machine Learning

Abstract:

Deep learning have achieved promising results on a wide spectrum of AI applications. Larger datasets and models consistently yield better performance. However, we generally spend longer training time on more computation and communication. In this survey, we aim to provide a clear sketch about the optimizations for large-scale deep learning with regard to the model accuracy and model efficiency. We investigate algorithms that are most commonly used for optimizing, elaborate the debatable topic of generalization gap arises in large-batch training, and review the SOTA strategies in addressing the communication overhead and reducing the memory footprints.

Keywords:  

Author(s) Name:  Xiaoxin He, Fuzhao Xue, Xiaozhe Ren, Yang You

Publisher name:  arXiv:2111.00856

DOI:  10.48550/arXiv.2111.00856

Volume Information:  

Paper Link:   https://arxiv.org/abs/2111.00856