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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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