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ORCIDJ. Travis Johnston
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- affiliation: University of South Carolina, Columbia, SC, USA
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2020 – today
- 2022
[j7]Ariel Keller Rorabaugh, Silvina Caíno-Lores
, Travis Johnston, Michela Taufer:
Building High-Throughput Neural Architecture Search Workflows via a Decoupled Fitness Prediction Engine. IEEE Trans. Parallel Distributed Syst. 33(11): 2913-2926 (2022)- 2021
- [j6]Francis J. Alexander, James A. Ang, Jenna A. Bilbrey, Jan Balewski, Tiernan Casey, Ryan Chard, Jong Choi, Sutanay Choudhury, Bert J. Debusschere, Anthony M. DeGennaro, Nikoli Dryden, J. Austin Ellis, Ian T. Foster, Cristina Garcia-Cardona, Sayan Ghosh, Peter Harrington, Yunzhi Huang, Shantenu Jha, Travis Johnston, Ai Kagawa, Ramakrishnan Kannan, Neeraj Kumar, Zhengchun Liu, Naoya Maruyama, Satoshi Matsuoka, Erin McCarthy, Jamaludin Mohd-Yusof, Peter Nugent, Yosuke Oyama, Thomas Proffen, David Pugmire, Sivasankaran Rajamanickam, Vinay Ramakrishnaiah, Malachi Schram, Sudip K. Seal, Ganesh Sivaraman, Christine Sweeney, Li Tan, Rajeev Thakur, Brian Van Essen, Logan T. Ward, Paul M. Welch, Michael Wolf, Sotiris S. Xantheas, Kevin G. Yager, Shinjae Yoo, Byung-Jun Yoon:
Co-design Center for Exascale Machine Learning Technologies (ExaLearn). Int. J. High Perform. Comput. Appl. 35(6): 598-616 (2021) - [c18]Maryam Parsa, Catherine D. Schuman, Nitin Rathi, Amirkoushyar Ziabari, Derek C. Rose, J. Parker Mitchell, J. Travis Johnston, Bill Kay, Steven R. Young, Kaushik Roy:
Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach. ICONS 2021: 14:1-14:8 - [i5]Ariel Keller Rorabaugh, Silvina Caíno-Lores, Michael R. Wyatt II, Travis Johnston, Michela Taufer:
PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search. CoRR abs/2101.04185 (2021) - 2020
- [c17]Cristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, Sudip K. Seal:
Structure Prediction from Neutron Scattering Profiles: A Data Sciences Approach. IEEE BigData 2020: 1147-1155 - [c16]Catherine D. Schuman, Steven R. Young, J. Parker Mitchell, J. Travis Johnston, Derek C. Rose, Bryan P. Maldonado, Brian C. Kaul:
Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency. IGSC (Workshops) 2020: 1-8 - [c15]Catherine D. Schuman, J. Parker Mitchell, J. Travis Johnston, Maryam Parsa, Bill Kay, Prasanna Date, Robert M. Patton:
Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems. IJCNN 2020: 1-10 - [i4]Mihaela Dimovska, Travis Johnston, Catherine D. Schuman, J. Parker Mitchell, Thomas E. Potok:
Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks. CoRR abs/2002.01406 (2020)
2010 – 2019
- 2019
- [j5]David E. Womble, Mallikarjun Shankar, Wayne Joubert, J. Travis Johnston, Jack C. Wells, Jeffrey A. Nichols:
Early experiences on Summit: Data analytics and AI applications. IBM J. Res. Dev. 63(6): 2:1-2:9 (2019) - [j4]Robert Searles, Stephen Herbein, Travis Johnston, Michela Taufer, Sunita Chandrasekaran:
Creating a portable, high-level graph analytics paradigm for compute and data-intensive applications. Int. J. High Perform. Comput. Netw. 13(1): 105-118 (2019) - [c14]Robert M. Patton, Shahira Abousamra, Dimitris Samaras, Joel H. Saltz, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Junghoon Chae, Le Hou:
Exascale Deep Learning to Accelerate Cancer Research. IEEE BigData 2019: 1488-1496 - [c13]Steven R. Young, Pravallika Devineni, Maryam Parsa, J. Travis Johnston, Bill Kay, Robert M. Patton, Catherine D. Schuman, Derek C. Rose, Thomas E. Potok:
Evolving Energy Efficient Convolutional Neural Networks. IEEE BigData 2019: 4479-4485 - [c12]Cristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, Katharine Page, Sudip K. Seal:
Learning to Predict Material Structure from Neutron Scattering Data. IEEE BigData 2019: 4490-4497 - [c11]Junghoon Chae, Catherine D. Schuman, Steven R. Young, J. Travis Johnston, Derek C. Rose, Robert M. Patton, Thomas E. Potok:
Visualization System for Evolutionary Neural Networks for Deep Learning. IEEE BigData 2019: 4498-4502 - [c10]Jeremy T. Johnston, Steven R. Young, Catherine D. Schuman, Junghoon Chae, Don D. March, Robert M. Patton, Thomas E. Potok:
Fine-Grained Exploitation of Mixed Precision for Faster CNN Training. MLHPC@SC 2019: 9-18 - [c9]Mihaela Dimovska, Travis Johnston, Catherine D. Schuman, J. Parker Mitchell, Thomas E. Potok:
Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks. UEMCON 2019: 433-439 - [c8]Mihaela Dimovska, Travis Johnston:
A Novel Pruning Method for Convolutional Neural Networks Based off Identifying Critical Filters. PEARC 2019: 63:1-63:7 - [i3]Robert M. Patton, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Junghoon Chae, Le Hou, Shahira Abousamra, Dimitris Samaras, Joel H. Saltz:
Exascale Deep Learning to Accelerate Cancer Research. CoRR abs/1909.12291 (2019) - 2018
- [c7]Robert M. Patton, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Don D. March, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Thomas P. Karnowski, Maxim A. Ziatdinov, Sergei V. Kalinin:
167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation. SC 2018: 50:1-50:11 - 2017
- [j3]Travis Johnston, Boyu Zhang, Adam Liwo, Silvia Crivelli, Michela Taufer:
In situ data analytics and indexing of protein trajectories. J. Comput. Chem. 38(16): 1419-1430 (2017) - [c6]J. Travis Johnston, Steven R. Young, David Hughes, Robert M. Patton, Devin White:
Optimizing Convolutional Neural Networks for Cloud Detection. MLHPC@SC 2017: 4:1-4:9 - [c5]Steven R. Young, Derek C. Rose, J. Travis Johnston, William T. Heller, Thomas P. Karnowski, Thomas E. Potok, Robert M. Patton, Gabriel N. Perdue, Jonathan A. Miller:
Evolving Deep Networks Using HPC. MLHPC@SC 2017: 7:1-7:7 - [i2]Sebastian M. Cioaba, Travis Johnston, Matt McGinnis:
Cospectral mates for the union of some classes in the Johnson association scheme. CoRR abs/1701.08747 (2017) - 2016
- [c4]Michael R. Wyatt II, Travis Johnston, Mia Papas, Michela Taufer:
Development of a Scalable Method for Creating Food Groups Using the NHANES Dataset and MapReduce. BCB 2016: 118-127 - [c3]Travis Johnston, Connor Zanin, Michela Taufer:
HYPPO: A Hybrid, Piecewise Polynomial Modeling Technique for Non-Smooth Surfaces. SBAC-PAD 2016: 26-33 - 2015
- [j2]J. Travis Johnston, Linyuan Lu, Kevin G. Milans:
Boolean algebras and Lubell functions. J. Comb. Theory A 136: 174-183 (2015) - [c2]Dylan Chapp, Travis Johnston, Michela Taufer:
On the Need for Reproducible Numerical Accuracy through Intelligent Runtime Selection of Reduction Algorithms at the Extreme Scale. CLUSTER 2015: 166-175 - [c1]Travis Johnston, Mohammad Alsulmi, Pietro Cicotti, Michela Taufer:
Performance Tuning of MapReduce Jobs Using Surrogate-based Modeling. ICCS 2015: 49-59 - [i1]Travis Johnston, Boyu Zhang, Adam Liwo, Silvia Crivelli, Michela Taufer:
It-Situ Data Analysis of Protein Folding Trajectories. CoRR abs/1510.08789 (2015) - 2014
- [j1]J. Travis Johnston, Linyuan Lu:
Turán Problems on Non-Uniform Hypergraphs. Electron. J. Comb. 21(4): 4 (2014)
Coauthor Index
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