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Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems


Sep 04, 2019
Hao-Jun Michael Shi, Dheevatsa Mudigere, Maxim Naumov, Jiyan Yang

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* 20 pages, 11 figures, 4 tables 

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Deep Learning Recommendation Model for Personalization and Recommendation Systems


May 31, 2019
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, Misha Smelyanskiy

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* 10 pages, 6 figures 

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A Progressive Batching L-BFGS Method for Machine Learning


May 30, 2018
Raghu Bollapragada, Dheevatsa Mudigere, Jorge Nocedal, Hao-Jun Michael Shi, Ping Tak Peter Tang

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* ICML 2018. 25 pages, 17 figures, 2 tables 

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A Primer on Coordinate Descent Algorithms


Jan 12, 2017
Hao-Jun Michael Shi, Shenyinying Tu, Yangyang Xu, Wotao Yin

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Practical Algorithms for Learning Near-Isometric Linear Embeddings


Apr 22, 2016
Jerry Luo, Kayla Shapiro, Hao-Jun Michael Shi, Qi Yang, Kan Zhu

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