J. Travis Johnston

According to our database, J. Travis Johnston authored at least 28 papers between 2014 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2022

Building High-Throughput Neural Architecture Search Workflows via a Decoupled Fitness Prediction Engine.

Ariel Keller Rorabaugh, Silvina Caíno-Lores, Travis Johnston, Michela Taufer

IEEE Trans. Parallel Distributed Syst., 2022

2021

Co-design Center for Exascale Machine Learning Technologies (ExaLearn).

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 Ramakrishniah, 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

Int. J. High Perform. Comput. Appl., 2021

PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search.

Ariel Keller Rorabaugh, Silvina Caíno-Lores, Michael R. Wyatt II, Travis Johnston, Michela Taufer

CoRR, 2021

Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach.

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

Proceedings of the ICONS 2021: International Conference on Neuromorphic Systems 2021, 2021

2020

Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems.

Catherine D. Schuman, J. Parker Mitchell, J. Travis Johnston, Maryam Parsa, Bill Kay, Prasanna Date, Robert M. Patton

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency.

Catherine D. Schuman, Steven R. Young, J. Parker Mitchell, J. Travis Johnston, Derek C. Rose, Bryan P. Maldonado, Brian C. Kaul

Proceedings of the 11th International Green and Sustainable Computing Workshops, 2020

Structure Prediction from Neutron Scattering Profiles: A Data Sciences Approach.

Cristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, Sudip K. Seal

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

Creating a portable, high-level graph analytics paradigm for compute and data-intensive applications.

Robert Searles, Stephen Herbein, Travis Johnston, Michela Taufer, Sunita Chandrasekaran

Int. J. High Perform. Comput. Netw., 2019

Early experiences on Summit: Data analytics and AI applications.

David E. Womble, Mallikarjun Shankar, Wayne Joubert, J. Travis Johnston, Jack C. Wells, Jeffrey A. Nichols

IBM J. Res. Dev., 2019

A Novel Pruning Method for Convolutional Neural Networks Based off Identifying Critical Filters.

Mihaela Dimovska, Travis Johnston

Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning), 2019

Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks.

Mihaela Dimovska, Travis Johnston, Catherine D. Schuman, J. Parker Mitchell, Thomas E. Potok

Proceedings of the 10th IEEE Annual Ubiquitous Computing, 2019

Fine-Grained Exploitation of Mixed Precision for Faster CNN Training.

Jeremy T. Johnston, Steven R. Young, Catherine D. Schuman, Junghoon Chae, Don D. March, Robert M. Patton, Thomas E. Potok

Proceedings of the 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments, 2019

Evolving Energy Efficient Convolutional Neural Networks.

Steven R. Young, Pravallika Devineni, Maryam Parsa, J. Travis Johnston, Bill Kay, Robert M. Patton, Catherine D. Schuman, Derek C. Rose, Thomas E. Potok

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Exascale Deep Learning to Accelerate Cancer Research.

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

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Learning to Predict Material Structure from Neutron Scattering Data.

Cristina Garcia-Cardona, Ramakrishnan Kannan, Travis Johnston, Thomas Proffen, Katharine Page, Sudip K. Seal

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Visualization System for Evolutionary Neural Networks for Deep Learning.

Junghoon Chae, Catherine D. Schuman, Steven R. Young, J. Travis Johnston, Derek C. Rose, Robert M. Patton, Thomas E. Potok

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation.

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

Proceedings of the International Conference for High Performance Computing, 2018

2017

<i>In situ</i> data analytics and indexing of protein trajectories.

Travis Johnston, Boyu Zhang, Adam Liwo, Silvia Crivelli, Michela Taufer

J. Comput. Chem., 2017

Cospectral mates for the union of some classes in the Johnson association scheme.

Sebastian M. Cioaba, Travis Johnston, Matt McGinnis

CoRR, 2017

Evolving Deep Networks Using HPC.

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

Proceedings of the Machine Learning on HPC Environments, 2017

Optimizing Convolutional Neural Networks for Cloud Detection.

J. Travis Johnston, Steven R. Young, David Hughes, Robert M. Patton, Devin White

Proceedings of the Machine Learning on HPC Environments, 2017

2016

HYPPO: A Hybrid, Piecewise Polynomial Modeling Technique for Non-Smooth Surfaces.

Travis Johnston, Connor Zanin, Michela Taufer

Proceedings of the 28th International Symposium on Computer Architecture and High Performance Computing, 2016

Development of a Scalable Method for Creating Food Groups Using the NHANES Dataset and MapReduce.

Michael R. Wyatt II, Travis Johnston, Mia Papas, Michela Taufer

Proceedings of the 7th ACM International Conference on Bioinformatics, 2016

2015

Boolean algebras and Lubell functions.

J. Travis Johnston, Linyuan Lu, Kevin G. Milans

J. Comb. Theory, Ser. A, 2015

It-Situ Data Analysis of Protein Folding Trajectories.

Travis Johnston, Boyu Zhang, Adam Liwo, Silvia Crivelli, Michela Taufer

CoRR, 2015

Performance Tuning of MapReduce Jobs Using Surrogate-based Modeling.

Travis Johnston, Mohammad Alsulmi, Pietro Cicotti, Michela Taufer

Proceedings of the International Conference on Computational Science, 2015

On the Need for Reproducible Numerical Accuracy through Intelligent Runtime Selection of Reduction Algorithms at the Extreme Scale.

Dylan Chapp, Travis Johnston, Michela Taufer

Proceedings of the 2015 IEEE International Conference on Cluster Computing, 2015

2014

Turán Problems on Non-Uniform Hypergraphs.

J. Travis Johnston, Linyuan Lu

Electron. J. Comb., 2014