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David S. Kung

3 accepted papers

2021

CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks

NeurIPS 2021poster

Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applications ages, it is now more critical than ever to increase software development productivity and modernize legacy applicat…

Cited by 327SourcecodeScholar
2020

Improving Efficiency in Large-Scale Decentralized Distributed Training

ICASSP 2020accepted

Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks. One drawback of (A)D-PSGD is that the spectral gap of the mixing matrix…

Cited by 0SourceScholar
2019

Distributed Deep Learning Strategies for Automatic Speech Recognition

ICASSP 2019accepted

In this paper, we propose and investigate a variety of distributed deep learning strategies for automatic speech recognition (ASR) and evaluate them with a state-of-the-art Long short-term memory (LSTM) acoustic model on the 2000-hour Switchboard (SWB2000), which is one of the most widely used datas…

Cited by 0SourceScholar