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J. Travis Johnston
Person information
- affiliation: University of South Carolina, Columbia, SC, USA
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2020 – today
- 2022
- [j7]Ariel Keller Rorabaugh, Silvina Caíno-Lores
, Travis Johnston
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
Co-design Center for Exascale Machine Learning Technologies (ExaLearn). Int. J. High Perform. Comput. Appl. 35(6): 598-616 (2021) - [c18]Maryam Parsa
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
Structure Prediction from Neutron Scattering Profiles: A Data Sciences Approach. IEEE BigData 2020: 1147-1155 - [c16]Catherine D. Schuman
Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency. IGSC (Workshops) 2020: 1-8 - [c15]Catherine D. Schuman
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
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
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
Exascale Deep Learning to Accelerate Cancer Research. IEEE BigData 2019: 1488-1496 - [c13]Steven R. Young, Pravallika Devineni, Maryam Parsa
Evolving Energy Efficient Convolutional Neural Networks. IEEE BigData 2019: 4479-4485 - [c12]Cristina Garcia-Cardona
Learning to Predict Material Structure from Neutron Scattering Data. IEEE BigData 2019: 4490-4497 - [c11]Junghoon Chae, Catherine D. Schuman
Visualization System for Evolutionary Neural Networks for Deep Learning. IEEE BigData 2019: 4498-4502 - [c10]Jeremy T. Johnston, Steven R. Young, Catherine D. Schuman
Fine-Grained Exploitation of Mixed Precision for Faster CNN Training. MLHPC@SC 2019: 9-18 - [c9]Mihaela Dimovska
Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks. UEMCON 2019: 433-439 - [c8]Mihaela Dimovska
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
In situ data analytics and indexing of protein trajectories. J. Comput. Chem. 38(16): 1419-1430 (2017) - [c6]J. Travis Johnston
Optimizing Convolutional Neural Networks for Cloud Detection. MLHPC@SC 2017: 4:1-4:9 - [c5]Steven R. Young
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
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
Boolean algebras and Lubell functions. J. Comb. Theory A 136: 174-183 (2015) - [c2]Dylan Chapp, Travis Johnston
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
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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