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Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale


May 26, 2021
Zhaoxia, Deng, Jongsoo Park, Ping Tak Peter Tang, Haixin Liu, Jie, Yang, Hector Yuen, Jianyu Huang, Daya Khudia, Xiaohan Wei, Ellie Wen, Dhruv Choudhary, Raghuraman Krishnamoorthi, Carole-Jean Wu, Satish Nadathur, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy


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Mixed-Precision Embedding Using a Cache


Oct 23, 2020
Jie Amy Yang, Jianyu Huang, Jongsoo Park, Ping Tak Peter Tang, Andrew Tulloch


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Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism


Oct 18, 2020
Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney

* 17 pages, 8 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

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

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Dictionary Learning by Dynamical Neural Networks


May 23, 2018
Tsung-Han Lin, Ping Tak Peter Tang


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Enabling Sparse Winograd Convolution by Native Pruning


Oct 13, 2017
Sheng Li, Jongsoo Park, Ping Tak Peter Tang

* 10 pages, 2 figures 

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Faster CNNs with Direct Sparse Convolutions and Guided Pruning


Jul 28, 2017
Jongsoo Park, Sheng Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, Pradeep Dubey

* 12 pages, 5 figures 

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Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results


May 15, 2017
Ping Tak Peter Tang, Tsung-Han Lin, Mike Davies

* 13 pages, 3 figures 

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On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima


Feb 09, 2017
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang

* Accepted as a conference paper at ICLR 2017 

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