A Fast and Stable Incremental Clustering Algorithm. - Steven R. Young; Itamar Arel; Thomas P. Karnowski; Derek C. Rose (ITNG, 2010)

Modeling Temporal Dynamics with Function Approximation in Deep Spatio-Temporal Inference Network. - Thomas P. Karnowski; Itamar Arel; Steven R. Young (BICA, 2011)

Recurrent Clustering for Unsupervised Feature Extraction with Application to Sequence Detection. - Steven Robert Young; Itamar Arel (ICMLA (2), 2012)

Recurrent Online Clustering as a Spatio-Temporal Feature Extractor in DeSTIN - Steven Robert Young; Itamar Arel (CoRR, 2013)

30.10 A 1TOPS/W analog deep machine-learning engine with floating-gate storage in 0.13μm CMOS. - Junjie Lu; Steven R. Young; Itamar Arel; Jeremy Holleman (ISSCC, 2014)

Hierarchical spatiotemporal feature extraction using recurrent online clustering. - Steven R. Young; Andrew S. Davis; Aaron Mishtal; Itamar Arel (Pattern Recognit. Lett., 2014)

On the Impact of Approximate Computation in an Analog DeSTIN Architecture. - Steven R. Young; Junjie Lu; Jeremy Holleman; Itamar Arel (IEEE Trans. Neural Networks Learn. Syst., 2014)

Analog inference circuits for deep learning. - Jeremy Holleman; Itamar Arel; Steven R. Young; Junjie Lu (BioCAS, 2015)

Optimizing deep learning hyper-parameters through an evolutionary algorithm. - Steven R. Young; Derek C. Rose; Thomas P. Karnowski; Seung-Hwan Lim; Robert M. Patton (MLHPC@SC, 2015)

A 1 TOPS/W Analog Deep Machine-Learning Engine With Floating-Gate Storage in 0.13 µm CMOS. - Junjie Lu; Steven R. Young; Itamar Arel; Jeremy Holleman (IEEE J. Solid State Circuits, 2015)

Vertex reconstruction of neutrino interactions using deep learning. - Adam M. Terwilliger; Gabriel N. Perdue; David Isele; Robert M. Patton; Steven R. Young (IJCNN, 2017)

Neuromorphic computing for temporal scientific data classification. - Catherine D. Schuman; Thomas E. Potok; Steven R. Young; Robert M. Patton; Gabriel N. Perdue; Gangotree Chakma; Austin Wyer; Garrett S. Rose (NCS, 2017)

Optimizing Convolutional Neural Networks for Cloud Detection. - J. Travis Johnston; Steven R. Young; David Hughes; Robert M. Patton; Devin White (MLHPC@SC, 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 (MLHPC@SC, 2017)

Unsupervised Identification of Study Descriptors in Toxicology Research: An Experimental Study. - Drahomira Herrmannova; Steven Robert Young; Robert M. Patton; Christopher G. Stahl; Nicole C. Kleinstreuer; Mary S. Wolfe (Louhi@EMNLP, 2018)

Neural Networks and Graph Algorithms with Next-Generation Processors. - Kathleen E. Hamilton; Catherine D. Schuman; Steven R. Young; Neena Imam; Travis S. Humble (IPDPS Workshops, 2018)

Deepmod: An Over-the-Air Trainable Machine Modem for Resilient PHY Layer Communications. - Adam L. Anderson; Steven R. Young; Thomas P. Karnowski; Jason M. Vann (MILCOM, 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 (SC, 2018)

Adiabatic Quantum Computation Applied to Deep Learning Networks. - 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 (Entropy, 2018)

A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers. - 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 (ACM J. Emerg. Technol. Comput. Syst., 2018)

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 (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 (IEEE BigData, 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 (IEEE BigData, 2019)

Deep Learning for Vertex Reconstruction of Neutrino-nucleus Interaction Events with Combined Energy and Time Data. - Linghao Song; Fan Chen 001; Steven R. Young; Catherine D. Schuman; Gabriel N. Perdue; Thomas E. Potok (ICASSP, 2019)

Inferring Convolutional Neural Networks' Accuracies from Their Architectural Characterizations. - Duc Hoang; Jesse Hamer; Gabriel N. Perdue; Steven R. Young; Jonathan A. Miller; Anushree Ghosh (ICMLA, 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 (MLHPC@SC, 2019)

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 (IGSC (Workshops), 2020)

Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment. - 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 001 (IJCNN, 2020)

Ensembles of Networks Produced from Neural Architecture Search. - Emily J. Herron; Steven R. Young; Thomas E. Potok (ISC Workshops, 2020)

Accelerating Scientific Computing in the Post-Moore's Era. - Kathleen E. Hamilton; Catherine D. Schuman; Steven R. Young; Ryan S. Bennink; Neena Imam; Travis S. Humble (ACM Trans. Parallel Comput., 2020)

Real-Time Evolution and Deployment of Neuromorphic Computing at The Edge. - Catherine D. Schuman; Steven R. Young; Bryan P. Maldonado; Brian C. Kaul (IGSC (Workshops), 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 001 (ICONS, 2021)

Next-Cycle Optimal Fuel Control for Cycle-to-Cycle Variability Reduction in EGR-Diluted Combustion. - Bryan P. Maldonado; Brian C. Kaul; Catherine D. Schuman; Steven R. Young; J. Parker Mitchell (IEEE Control. Syst. Lett., 2021)

ICDARTS: Improving the Stability of Cyclic DARTS. - Emily J. Herron; Steven R. Young; Derek C. Rose (ICMLA, 2022)

Neuromorphic Computing for Scientific Applications. - 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 (RSDHA@SC, 2022)

Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators. - Bryan P. Maldonado; Brian C. Kaul; Catherine D. Schuman; Steven R. Young; J. Parker Mitchell (IEEE Trans. Control. Syst. Technol., 2022)

Efficacy of using a dynamic length representation vs. a fixed-length for neuroarchitecture search. - Mark Coletti; Chathika Gunaratne; Steven R. Young; Swetha Varadarajan; Robert M. Patton; Thomas E. Potok (GECCO Companion, 2024)