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Junqi Yin

Atomic structure generation from reconstructing structural fingerprints

Jul 27, 2022
Victor Fung, Shuyi Jia, Jiaxin Zhang, Sirui Bi, Junqi Yin, P. Ganesh

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* 16 pages and 9 figures in the main text

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Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks

Jul 25, 2022
Massimiliano Lupo Pasini, Junqi Yin

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* 22 pages; 9 figures

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MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Oct 26, 2021
Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin

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Data optimization for large batch distributed training of deep neural networks

Dec 18, 2020
Shubhankar Gahlot, Junqi Yin, Mallikarjun Shankar

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* Computational Science & Computational Intelligence (CSCI'20), 7 pages

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Distributed Training and Optimization Of Neural Networks

Dec 03, 2020
Jean-Roch Vlimant, Junqi Yin

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* 20 pages, 4 figures, 2 tables, Submitted for review. To appear in "Artificial Intelligence for Particle Physics", World Scientific Publishing

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Exascale Deep Learning for Scientific Inverse Problems

Sep 24, 2019
Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson

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* 13 pages, 9 figures. Under review by the Systems and Machine Learning (SysML) Conference (SysML '20)

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