Search Results for author: Jiecao Yu

Found 3 papers, 0 papers with code

Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems

no code implementations4 May 2021 Xiaocong Du, Bhargav Bhushanam, Jiecao Yu, Dhruv Choudhary, Tianxiang Gao, Sherman Wong, Louis Feng, Jongsoo Park, Yu Cao, Arun Kejariwal

Our method leverages structured sparsification to reduce computational cost without hurting the model capacity at the end of offline training so that a full-size model is available in the recurring training stage to learn new data in real-time.

Recommendation Systems

Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data

no code implementations16 Oct 2020 Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen, Zeliang Chen, Jiyan Yang, Jongsoo Park, Qiang Liu, Arun Kejariwal

To the best of our knowledge, this is the first work to provide in-depth analysis and discussion of applying pruning to online recommendation systems with non-stationary data distribution.

Recommendation Systems

Spatial-Winograd Pruning Enabling Sparse Winograd Convolution

no code implementations ICLR 2019 Jiecao Yu, Jongsoo Park, Maxim Naumov

To achieve a high Winograd-domain weight sparsity without changing network structures, we propose a new pruning method, spatial-Winograd pruning.

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