Data Science III: Deep Learning
FOCUS · R20 · ID: 355165
Presentations
-
Deep Learning-enabled Computational Microscopy and Sensing
Invited
–
Presenters
-
Aydogan Ozcan
- University of California, Los Angeles
Authors
-
Aydogan Ozcan
- University of California, Los Angeles
-
-
Exploring Organic Ferroelectrics Using Data-driven Approaches
ORAL
–
Presenters
-
Ayana Ghosh
- Univ of Connecticut - Storrs
- Materials Science and Engineering, University of Connecticut
- University of Connecticut
Authors
-
Ayana Ghosh
- Univ of Connecticut - Storrs
- Materials Science and Engineering, University of Connecticut
- University of Connecticut
-
Nicholas Lubbers
- Computer, Computational and Statistical Sciences, Information Sciences, Los Alamos National Laboratory
- Computer Computational Statistical Sciences, Los Alamos National Laboratory
-
Serge M Nakhmanson
- Univ of Connecticut - Storrs
-
Jian-Xin Zhu
- Los Alamos National Laboratory
- Los Alamos National Lab
- Los Alamos Natl Lab
- Theoretical Division, Los Alamos National Laboratory
-
-
Deep Learning Model for Finding New Superconductors
ORAL
–
Presenters
-
Tomohiko Konno
- National Institute of Information and Communications Technology
Authors
-
Tomohiko Konno
- National Institute of Information and Communications Technology
-
Hodaka Kurokawa
- University of Tokyo
-
Fuyuki Nabeshima
- University of Tokyo
- Dept. of Basic Science, Univ. of Tokyo
- Univ of Tokyo
-
Yuki Sakishita
- University of Tokyo
- Dept. of Basic Science, Univ. of Tokyo
- Univ of Tokyo
-
Ryo Ogawa
- University of Tokyo
- Dept. of Basic Sci., Univ. Tokyo
-
Iwao Hosako
- National Institute of Information and Communications Technology
-
Atsutaka Maeda
- University of Tokyo
- Dept. of Basic Science, Univ. of Tokyo
- Univ of Tokyo
- Dept. of Basic Sci., Univ. Tokyo
-
-
Deep Learning for Energetic Materials: Predicting Material Properties from Electronic Structure using Convolutional Neural Networks
ORAL
–
Presenters
-
Alex Casey
- Mechanical Engineering, Purdue University
Authors
-
Alex Casey
- Mechanical Engineering, Purdue University
-
Brian Barnes
- Army Research Laboratory
- Detonation Science and Modeling Branch, CCDC Army Research Laboratory
- CCDC Army Research Laboratory
- US Army Rsch Lab - Aberdeen
-
Ilias Bilionis
- Mechanical Engineering, Purdue University
-
Steven F. Son
- Mechanical Engineering, Purdue University
- Purdue University
-
-
Optimization of Molecular Characteristic using Continuous Representation of Molecules by Variational Autoencoder with Discriminator
ORAL
–
Presenters
-
Kyosuke Sato
- Graduate School of Natural Science and Technology, Okayama University
- Okayama Univ
Authors
-
Kyosuke Sato
- Graduate School of Natural Science and Technology, Okayama University
- Okayama Univ
-
Kenji Tsuruta
- Graduate School of Natural Science and Technology, Okayama University
- Okayama Univ
-
-
An Initial Design-based Deep Learning Procedure for the Optimization of High Dimensional ReaxFF Parameters
ORAL
–
Presenters
-
Mert Yigit Sengul
- Materials Science and Engineering, The Pennsylvania State University
- Pennsylvania State University
Authors
-
Mert Yigit Sengul
- Materials Science and Engineering, The Pennsylvania State University
- Pennsylvania State University
-
Yao Song
- Department of Statistics, Rutgers University
-
Linglin He
- Department of Statistics, Rutgers University
-
Ying Hung
- Department of Statistics, Rutgers University
-
Tirthankar Dasgupta
- Department of Statistics, Rutgers University
-
Adri C.T. van Duin
- Department of Mechanical Engineering, Penn State University
- Pennsylvania State University
- Mechanical Engineering, Pennsylvania State University
-
-
Feature Extraction Using Semi-Supervised Deep Learning.
ORAL
–
Presenters
-
Muammar El Khatib
- Computational Research Division, Lawrence Berkeley National Laboratory
Authors
-
Muammar El Khatib
- Computational Research Division, Lawrence Berkeley National Laboratory
-
Wibe A De Jong
- Computational Research Division, Lawrence Berkeley National Laboratory
- Lawrence Berkeley National Laboratory
- Computational Chemistry, Materials and Climate Group, Lawrence Berkeley National Laboratory
-
-
Unsupervised feature extraction in simple physical models through mutual information maximization
ORAL
–
Presenters
-
Leopoldo Sarra
- Max Planck Inst for Sci Light
Authors
-
Leopoldo Sarra
- Max Planck Inst for Sci Light
-
Florian Marquardt
- Max Planck Inst for Sci Light
- Max Planck Institute for the Science of Light
-
-
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
ORAL
–
Presenters
-
Samuel Kim
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology
Authors
-
Samuel Kim
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology
-
Peter Lu
- Physics, Massachusetts Institute of Technology
- Department of Physics, Massachusetts Institute of Technology
-
Michael Gilbert
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology
-
Srijon Mukherjee
- Physics, Massachusetts Institute of Technology
-
Li Jing
- Physics, Massachusetts Institute of Technology
-
Vladimir Čeperić
- University of Zagreb
-
Marin Soljacic
- Physics, Massachusetts Institute of Technology
- Department of Physics, Massachusetts Institute of Technology
-
-
Rapid machine learning-based solutions of partial differential equations on complex domains.
ORAL
–
Presenters
-
Vikas Dwivedi
- Indian Inst of Tech-Madras
Authors
-
Vikas Dwivedi
- Indian Inst of Tech-Madras
-
Balaji Srinivasan
- Indian Inst of Tech-Madras
-
-
Probabilistically-autoencoded horseshoe-disentangled multidomain item-response theory models
ORAL
–
Presenters
-
Joshua Chang
- National Institutes of Health - NIH
Authors
-
Joshua Chang
- National Institutes of Health - NIH
-
Shashaank Vattikuti
- National Institutes of Health - NIH
-
Carson C Chow
- National Institutes of Health - NIH
-
-
Turbulence-generating networks
ORAL
–
Presenters
-
Armando Garcia
- University of Texas, El Paso
Authors
-
Armando Garcia
- University of Texas, El Paso
-
Rao Gudimetla
- Air Force Research Laboratory
- Air Force Research Lab
-
Jorge Munoz
- University of Texas, El Paso
-
-
SignalTrain: Modeling Time-dependent Nonlinear Signal Processing Effects Using Deep Neural Networks
ORAL
–
Presenters
-
William Mitchell
- Physics, Belmont University
Authors
-
William Mitchell
- Physics, Belmont University
-
Scott Hawley
- Physics, Belmont University
-