24 citations · 40 across the 9 of their papers we have counts for
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eess.AS2021
Performance optimizations on deep noise suppression models
Jerry Chee, Sebastian Braun, Vishak Gopal +1
We study the role of magnitude structured pruning as an architecture search to speed up the inference time of a deep noise suppression (DNS) model. While deep learning approaches h…
cs.LG2021★ 4 cited
Model Preserving Compression for Neural Networks
Jerry Chee, Megan Renz, Anil Damle +1
After training complex deep learning models, a common task is to compress the model to reduce compute and storage demands. When compressing, it is desirable to preserve the origina…
cs.LG2021★ 2 cited
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Chengrun Yang, Ziyang Wu, Jerry Chee +2
Low-precision arithmetic trains deep learning models using less energy, less memory and less time. However, we pay a price for the savings: lower precision may yield larger round-o…