30 citations · 60 across the 4 of their papers we have counts for
8 papers
Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data
Massimiliano Lupo Pasini, Vittorio Gabbi, Junqi Yin +2
We propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discri…
Application of variational policy gradient to atomic-scale materials synthesis
Siyan Liu, Nikolay Borodinov, Lukas Vlcek +3
Atomic-scale materials synthesis via layer deposition techniques present a unique opportunity to control material structures and yield systems that display unique functional proper…
Exascale Deep Learning for Scientific Inverse Problems
Nouamane Laanait, Joshua Romero, Junqi Yin +6
We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grou…
Competing Phases in Epitaxial Vanadium Dioxide at Nanoscale
Yogesh Sharma, Martin V. Holt, Nouamane Laanait +11
Phase competition in correlated oxides offers tantalizing opportunities as many intriguing physical phenomena occur near the phase transitions. Owing to a sharp metal-insulator tra…
USID and Pycroscopy -- Open frameworks for storing and analyzing spectroscopic and imaging data
Suhas Somnath, Chris R. Smith, Nouamane Laanait +7
Materials science is undergoing profound changes due to advances in characterization instrumentation that have resulted in an explosion of data in terms of volume, velocity, variet…
Reconstruction of 3-D Atomic Distortions from Electron Microscopy with Deep Learning
Nouamane Laanait, Qian He, Albina Y. Borisevich
Deep learning has demonstrated superb efficacy in processing imaging data, yet its suitability in solving challenging inverse problems in scientific imaging has not been fully expl…