Steven R. Young

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 34

Top Topics

Electron Microscopy

Scientific Data

Convolutional Neural Networks

Top Venues

Publications

Mark Coletti, Chathika Gunaratne, Steven R. Young, Swetha Varadarajan, Robert M. Patton, Thomas E. Potok
Efficacy of using a dynamic length representation vs. a fixed-length for neuroarchitecture search. GECCO Companion (2024)
Bryan P. Maldonado, Brian C. Kaul, Catherine D. Schuman, Steven R. Young, J. Parker Mitchell
Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators. IEEE Trans. Control. Syst. Technol. 30 (6) (2022)
Robert M. Patton, Prasanna Date, Shruti R. Kulkarni, Chathika Gunaratne, Seung-Hwan Lim, Guojing Cong, Steven R. Young, Mark Coletti, Thomas E. Potok, Catherine D. Schuman
Neuromorphic Computing for Scientific Applications. RSDHA@SC (2022)
Emily J. Herron, Steven R. Young, Derek C. Rose
ICDARTS: Improving the Stability of Cyclic DARTS. ICMLA (2022)
Catherine D. Schuman, Steven R. Young, Bryan P. Maldonado, Brian C. Kaul
Real-Time Evolution and Deployment of Neuromorphic Computing at The Edge. IGSC (2021)
Bryan P. Maldonado, Brian C. Kaul, Catherine D. Schuman, Steven R. Young, J. Parker Mitchell
Next-Cycle Optimal Fuel Control for Cycle-to-Cycle Variability Reduction in EGR-Diluted Combustion. ACC (2021)
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)
Bryan P. Maldonado, Brian C. Kaul, Catherine D. Schuman, Steven R. Young, J. Parker Mitchell
Next-Cycle Optimal Fuel Control for Cycle-to-Cycle Variability Reduction in EGR-Diluted Combustion. IEEE Control. Syst. Lett. 5 (6) (2021)
Emily J. Herron, Steven R. Young, Thomas E. Potok
Ensembles of Networks Produced from Neural Architecture Search. ISC Workshops (2020)
Maryam Parsa, Catherine D. Schuman, Prasanna Date, Derek C. Rose, Bill Kay, J. Parker Mitchell, Steven R. Young, Ryan Dellana, William Severa, Thomas E. Potok, Kaushik Roy
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment. CoRR (2020)
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)
Kathleen E. Hamilton, Catherine D. Schuman, Steven R. Young, Ryan S. Bennink, Neena Imam, Travis S. Humble
Accelerating Scientific Computing in the Post-Moore's Era. ACM Trans. Parallel Comput. 7 (1) (2020)
Maryam Parsa, Catherine D. Schuman, Prasanna Date, Derek C. Rose, Bill Kay, J. Parker Mitchell, Steven R. Young, Ryan Dellana, William Severa, Thomas E. Potok, Kaushik Roy
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment. IJCNN (2020)
Duc Hoang, Jesse Hamer, Gabriel N. Perdue, Steven R. Young, Jonathan A. Miller, Anushree Ghosh
Inferring Convolutional Neural Networks' accuracies from their architectural characterizations. CoRR (2020)
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)
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 (2019)
Linghao Song, Fan Chen, Steven R. Young, Catherine D. Schuman, Gabriel N. Perdue, Thomas E. Potok
Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data. CoRR (2019)
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)
Duc Hoang, Jesse Hamer, Gabriel N. Perdue, Steven R. Young, Jonathan A. Miller, Anushree Ghosh
Inferring Convolutional Neural Networks' Accuracies from Their Architectural Characterizations. ICMLA (2019)
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)
Linghao Song, Fan Chen, Steven R. Young, Catherine D. Schuman, Gabriel N. Perdue, Thomas E. Potok
Deep Learning for Vertex Reconstruction of Neutrino-nucleus Interaction Events with Combined Energy and Time Data. ICASSP (2019)
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)
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)
Thomas E. Potok, Catherine D. Schuman, Steven R. Young, Robert M. Patton, Federico M. Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett S. Rose, Gangotree Chakma
A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers. ACM J. Emerg. Technol. Comput. Syst. 14 (2) (2018)
Kathleen E. Hamilton, Catherine D. Schuman, Steven R. Young, Neena Imam, Travis S. Humble
Neural Networks and Graph Algorithms with Next-Generation Processors. IPDPS Workshops (2018)
Jeremy Liu, Federico M. Spedalieri, Ke-Thia Yao, Thomas E. Potok, Catherine D. Schuman, Steven R. Young, Robert M. Patton, Garrett S. Rose, Gangotree Chakma
Adiabatic Quantum Computation Applied to Deep Learning Networks. Entropy 20 (5) (2018)
Drahomira Herrmannova, Steven R. Young, Robert M. Patton, Christopher G. Stahl, Nicole C. Kleinstreuer, Mary S. Wolfe
Unsupervised Identification of Study Descriptors in Toxicology Research: An Experimental Study. CoRR (2018)
Adam L. Anderson, Steven R. Young, Thomas P. Karnowski, Jason M. Vann
Deepmod: An Over-the-Air Trainable Machine Modem for Resilient PHY Layer Communications. MILCOM (2018)
Adam M. Terwilliger, Gabriel N. Perdue, David Isele, Robert M. Patton, Steven R. Young
Vertex reconstruction of neutrino interactions using deep learning. IJCNN (2017)
Thomas E. Potok, Catherine D. Schuman, Steven R. Young, Robert M. Patton, Federico M. Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett S. Rose, Gangotree Chakma
A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers. CoRR (2017)
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)
Catherine D. Schuman, Thomas E. Potok, Steven R. Young, Robert M. Patton, Gabriel N. Perdue, Gangotree Chakma, Austin Wyer, Garrett S. Rose
Neuromorphic computing for temporal scientific data classification. NCS (2017)
J. Travis Johnston, Steven R. Young, David Hughes, Robert M. Patton, Devin White
Optimizing Convolutional Neural Networks for Cloud Detection. MLHPC@SC (2017)
Thomas E. Potok, Catherine D. Schuman, Steven R. Young, Robert M. Patton, Federico M. Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett S. Rose, Gangotree Chakma
A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers. MLHPC@SC (2016)
Steven R. Young, Derek C. Rose, Thomas P. Karnowski, Seung-Hwan Lim, Robert M. Patton
Optimizing deep learning hyper-parameters through an evolutionary algorithm. MLHPC@SC (2015)
Jeremy Holleman, Itamar Arel, Steven R. Young, Junjie Lu
Analog inference circuits for deep learning. BioCAS (2015)
Junjie Lu, Steven R. Young, Itamar Arel, Jeremy Holleman
A 1 TOPS/W Analog Deep Machine-Learning Engine With Floating-Gate Storage in 0.13 µm CMOS. IEEE J. Solid State Circuits 50 (1) (2015)
Junjie Lu, Steven R. Young, Itamar Arel, Jeremy Holleman
30.10 A 1TOPS/W analog deep machine-learning engine with floating-gate storage in 0.13μm CMOS. ISSCC (2014)
Steven R. Young, Andrew S. Davis, Aaron Mishtal, Itamar Arel
Hierarchical spatiotemporal feature extraction using recurrent online clustering. Pattern Recognit. Lett. 37 (2014)
Steven R. Young, Junjie Lu, Jeremy Holleman, Itamar Arel
On the Impact of Approximate Computation in an Analog DeSTIN Architecture. IEEE Trans. Neural Networks Learn. Syst. 25 (5) (2014)
Thomas P. Karnowski, Itamar Arel, Steven R. Young
Modeling Temporal Dynamics with Function Approximation in Deep Spatio-Temporal Inference Network. BICA (2011)
Steven R. Young, Itamar Arel, Thomas P. Karnowski, Derek C. Rose
A Fast and Stable Incremental Clustering Algorithm. ITNG (2010)