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Andrey Malevich

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Decoupling the Depth and Scope of Graph Neural Networks

Jan 19, 2022
Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen

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Deep Graph Neural Networks with Shallow Subgraph Samplers

Dec 02, 2020
Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna, Long Jin, Andrey Malevich, Ren Chen

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Time-based Sequence Model for Personalization and Recommendation Systems

Aug 27, 2020
Tigran Ishkhanov, Maxim Naumov, Xianjie Chen, Yan Zhu, Yuan Zhong, Alisson Gusatti Azzolini, Chonglin Sun, Frank Jiang, Andrey Malevich, Liang Xiong

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Post-Training 4-bit Quantization on Embedding Tables

Nov 05, 2019
Hui Guan, Andrey Malevich, Jiyan Yang, Jongsoo Park, Hector Yuen

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The Architectural Implications of Facebook's DNN-based Personalized Recommendation

Jun 18, 2019
Udit Gupta, Xiaodong Wang, Maxim Naumov, Carole-Jean Wu, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang

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Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

Nov 29, 2018
Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Khudia, James Law, Parth Malani, Andrey Malevich, Satish Nadathur, Juan Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy

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