J. Travis Johnston

Orcid: 0000-0001-7108-1934

Affiliations:
  • University of South Carolina, Columbia, SC, USA


According to our database1, J. Travis Johnston authored at least 28 papers between 2014 and 2022.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2022
Building High-Throughput Neural Architecture Search Workflows via a Decoupled Fitness Prediction Engine.
IEEE Trans. Parallel Distributed Syst., 2022

2021
Co-design Center for Exascale Machine Learning Technologies (ExaLearn).
Int. J. High Perform. Comput. Appl., 2021

PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search.
CoRR, 2021

Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach.
Proceedings of the ICONS 2021: International Conference on Neuromorphic Systems 2021, 2021

2020
Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency.
Proceedings of the 11th International Green and Sustainable Computing Workshops, 2020

Structure Prediction from Neutron Scattering Profiles: A Data Sciences Approach.
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.
Int. J. High Perform. Comput. Netw., 2019

Early experiences on Summit: Data analytics and AI applications.
IBM J. Res. Dev., 2019

A Novel Pruning Method for Convolutional Neural Networks Based off Identifying Critical Filters.
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.
Proceedings of the 10th IEEE Annual Ubiquitous Computing, 2019

Fine-Grained Exploitation of Mixed Precision for Faster CNN Training.
Proceedings of the 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments, 2019

Evolving Energy Efficient Convolutional Neural Networks.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Exascale Deep Learning to Accelerate Cancer Research.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Learning to Predict Material Structure from Neutron Scattering Data.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Visualization System for Evolutionary Neural Networks for Deep Learning.
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.
Proceedings of the International Conference for High Performance Computing, 2018

2017
<i>In situ</i> data analytics and indexing of protein trajectories.
J. Comput. Chem., 2017

Cospectral mates for the union of some classes in the Johnson association scheme.
CoRR, 2017

Evolving Deep Networks Using HPC.
Proceedings of the Machine Learning on HPC Environments, 2017

Optimizing Convolutional Neural Networks for Cloud Detection.
Proceedings of the Machine Learning on HPC Environments, 2017

2016
HYPPO: A Hybrid, Piecewise Polynomial Modeling Technique for Non-Smooth Surfaces.
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.
Proceedings of the 7th ACM International Conference on Bioinformatics, 2016

2015
Boolean algebras and Lubell functions.
J. Comb. Theory A, 2015

It-Situ Data Analysis of Protein Folding Trajectories.
CoRR, 2015

Performance Tuning of MapReduce Jobs Using Surrogate-based Modeling.
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.
Proceedings of the 2015 IEEE International Conference on Cluster Computing, 2015

2014
Turán Problems on Non-Uniform Hypergraphs.
Electron. J. Comb., 2014


  Loading...