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Selective Network Linearization for Efficient Private Inference


Feb 04, 2022
Minsu Cho, Ameya Joshi, Siddharth Garg, Brandon Reagen, Chinmay Hegde


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Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning


Jul 26, 2021
Karthik Garimella, Nandan Kumar Jha, Brandon Reagen

* 2 figures and 2 tables 

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Sphynx: ReLU-Efficient Network Design for Private Inference


Jun 17, 2021
Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, Chinmay Hegde


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Circa: Stochastic ReLUs for Private Deep Learning


Jun 15, 2021
Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg


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Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors


May 09, 2021
Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim, Tianwei Shen, Meghan Cowan, Rajvi Shah, Caroline Trippel, Brandon Reagen, Timothy Sherwood, Vasileios Balntas, Armin Alaghi, Eddy Ilg

* 13 pages 

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DeepReDuce: ReLU Reduction for Fast Private Inference


Mar 02, 2021
Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen

* 12 pages, 5 Figures 

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CryptoNAS: Private Inference on a ReLU Budget


Jun 15, 2020
Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen, Siddharth Garg


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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

* 11 pages 

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Weightless: Lossy Weight Encoding For Deep Neural Network Compression


Nov 13, 2017
Brandon Reagen, Udit Gupta, Robert Adolf, Michael M. Mitzenmacher, Alexander M. Rush, Gu-Yeon Wei, David Brooks


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