Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox

Atomic structure generation from reconstructing structural fingerprints


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

Add code

* 16 pages and 9 figures in the main text 

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks


Jul 25, 2022
Massimiliano Lupo Pasini, Junqi Yin

Add code

* 22 pages; 9 figures 

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

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

Add code


   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Stable Anderson Acceleration for Deep Learning


Oct 26, 2021
Massimiliano Lupo Pasini, Junqi Yin, Viktor Reshniak, Miroslav Stoyanov

Add code


   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Neural network based order parameter for phase transitions and its applications in high-entropy alloys


Sep 12, 2021
Junqi Yin, Zongrui Pei, Michael Gao

Add code


   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data


Feb 21, 2021
Massimiliano Lupo Pasini, Vittorio Gabbi, Junqi Yin, Simona Perotto, Nouamane Laanait

Add code


   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Data optimization for large batch distributed training of deep neural networks


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

Add code

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

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Distributed Training and Optimization Of Neural Networks


Dec 03, 2020
Jean-Roch Vlimant, Junqi Yin

Add code

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

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Integrating Deep Learning in Domain Sciences at Exascale


Nov 23, 2020
Rick Archibald, Edmond Chow, Eduardo D'Azevedo, Jack Dongarra, Markus Eisenbach, Rocco Febbo, Florent Lopez, Daniel Nichols, Stanimire Tomov, Kwai Wong, Junqi Yin

Add code


   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

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

Add code

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

   Access Paper or Ask Questions

  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email
1
2
>>