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2020 – today
- 2024
[c61]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: 1888-1894- 2023
- [c60]Prasanna Date, Chathika Gunaratne, Shruti R. Kulkarni, Robert M. Patton, Mark Coletti, Thomas E. Potok:
SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing. ICONS 2023: 40:1-40:4 - [i7]Prasanna Date, Chathika Gunaratne, Shruti R. Kulkarni, Robert M. Patton, Mark Coletti, Thomas E. Potok:
SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing. CoRR abs/2305.02510 (2023) - 2022
- [j10]Catherine D. Schuman, Robert M. Patton, Shruti R. Kulkarni, Maryam Parsa, Christopher G. Stahl, Nicholas Quentin Haas, J. Parker Mitchell, Shay Snyder, Amelie Nagle, Alexandra Shanafield, Thomas E. Potok:
Evolutionary vs imitation learning for neuromorphic control at the edge. Neuromorph. Comput. Eng. 2(1): 14002 (2022) - [c59]Chathika Gunaratne, Robert M. Patton:
Genetic programming for understanding cognitive biases that generate polarization in social networks. GECCO Companion 2022: 546-549 - [c58]Mark Coletti, Chathika Gunaratne, Catherine D. Schuman, Robert M. Patton:
Training reinforcement learning models via an adversarial evolutionary algorithm. ICPP Workshops 2022: 23:1-23:6 - [c57]Catherine D. Schuman, James S. Plank, Robert M. Patton, Thomas E. Potok, Garrett S. Rose:
A Framework to Enable Top-Down Co-Design of Neuromorphic Systems for Real-World Applications. NICE 2022: 84-85 - [c56]Ramakrishnan Kannan, Piyush Sao, Hao Lu, Jakub Kurzak, Gundolf Schenk, Yongmei Shi, Seung-Hwan Lim, Sharat Israni, Vijay Thakkar, Guojing Cong, Robert M. Patton, Sergio E. Baranzini, Richard W. Vuduc, Thomas E. Potok:
Exaflops Biomedical Knowledge Graph Analytics. SC 2022: 6:1-6:11 - [c55]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: 22-28 - 2021
- [j9]Tirthankar Ghosal, Piyush Tiwary, Robert M. Patton, Christopher G. Stahl:
Towards establishing a research lineage via identification of significant citations. Quant. Sci. Stud. 2(4): 1511-1528 (2021) - [c54]Seung-Hwan Lim, Junghoon Chae, Guojing Cong, Drahomira Herrmannova, Robert M. Patton, Ramakrishnan Kannan, Thomas E. Potok:
Visual Understanding of COVID-19 Knowledge Graph for Predictive Analysis. IEEE BigData 2021: 4381-4386 - [c53]Mark A. Coletti, Shang Gao, Spencer Paulissen, Nicholas Quentin Haas, Robert M. Patton:
Diagnosing autonomous vehicle driving criteria with an adversarial evolutionary algorithm. GECCO Companion 2021: 301-302 - [c52]Prasanna Date, Bill Kay, Catherine D. Schuman, Robert M. Patton, Thomas E. Potok:
Computational Complexity of Neuromorphic Algorithms. ICONS 2021: 8:1-8:7 - [c51]Robert M. Patton, Catherine D. Schuman, Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, Nicholas Quentin Haas, Christopher G. Stahl, Spencer Paulissen, Prasanna Date, Thomas E. Potok, Shay Sneider:
Neuromorphic Computing for Autonomous Racing. ICONS 2021: 23:1-23:5 - [c50]Shang Gao, Spencer Paulissen, Mark Coletti, Robert M. Patton:
Quantitative Evaluation of Autonomous Driving in CARLA. IV Workshops 2021: 257-263 - [c49]Drahomira Herrmannova, Chathika Gunaratne, Vickie Walker, Andrew A. Rooney, Robert M. Patton, Mary Wolfe, Charles Schmitt:
Weak Supervision for Scientific Document Relevance Tagging. JCDL 2021: 338-339 - [e2]Thomas E. Potok, Melika Payvand, Catherine D. Schuman, Prasanna Date, Mutsumi Kimura, Cory E. Merkel, Brad Aimone, Sonia M. Buckley, Yiran Chen, Gregory Cohen, Todd Hylton, Robert M. Patton, Robinson E. Pino, Garrett S. Rose:
ICONS 2021: International Conference on Neuromorphic Systems 2021, Knoxville, TN, USA, July 27-29, 2021. ACM 2021, ISBN 978-1-4503-8691-3 [contents] - [i6]James S. Plank, Catherine D. Schuman, Robert M. Patton:
An Oracle and Observations for the OpenAI Gym / ALE Freeway Environment. CoRR abs/2109.01220 (2021) - 2020
- [c48]Catherine D. Schuman, J. Parker Mitchell, J. Travis Johnston, Maryam Parsa, Bill Kay, Prasanna Date, Robert M. Patton:
Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems. IJCNN 2020: 1-10 - [c47]Catherine D. Schuman, J. Parker Mitchell, Maryam Parsa, James S. Plank, Samuel D. Brown, Garrett S. Rose, Robert M. Patton, Thomas E. Potok:
Automated Design of Neuromorphic Networks for Scientific Applications at the Edge. IJCNN 2020: 1-7 - [c46]Hui Guan, Laxmikant Kishor Mokadam, Xipeng Shen, Seung-Hwan Lim, Robert M. Patton:
FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks. MLSys 2020 - [c45]Catherine D. Schuman, J. Parker Mitchell, Robert M. Patton, Thomas E. Potok, James S. Plank:
Evolutionary Optimization for Neuromorphic Systems. NICE 2020: 2:1-2:9 - [c44]J. Parker Mitchell, Catherine D. Schuman, Robert M. Patton, Thomas E. Potok:
Caspian: A Neuromorphic Development Platform. NICE 2020: 8:1-8:6 - [c43]Ramakrishnan Kannan, Piyush Sao, Hao Lu, Drahomira Herrmannova, Vijay Thakkar, Robert M. Patton, Richard W. Vuduc, Thomas E. Potok:
Scalable knowledge graph analytics at 136 petaflop/s. SC 2020: 6 - [e1]Muthu Kumar Chandrasekaran, Anita de Waard, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Eduard H. Hovy, Petr Knoth, David Konopnicki, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer:
Proceedings of the First Workshop on Scholarly Document Processing, SDP@EMNLP 2020, Online, November 19, 2020. Association for Computational Linguistics 2020, ISBN 978-1-952148-70-5 [contents]
2010 – 2019
- 2019
- [j8]Prasanna Date, Robert M. Patton, Catherine D. Schuman, Thomas E. Potok:
Efficiently embedding QUBO problems on adiabatic quantum computers. Quantum Inf. Process. 18(4): 117 (2019) - [c42]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: 1488-1496 - [c41]Maryam Parsa, J. Parker Mitchell, Catherine D. Schuman, Robert M. Patton, Thomas E. Potok, Kaushik Roy:
Bayesian-based Hyperparameter Optimization for Spiking Neuromorphic Systems. IEEE BigData 2019: 4472-4478 - [c40]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: 4479-4485 - [c39]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: 4498-4502 - [c38]Catherine D. Schuman, James S. Plank, Robert M. Patton, Thomas E. Potok:
Island model for parallel evolutionary optimization of spiking neuromorphic computing. GECCO (Companion) 2019: 306-307 - [c37]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: 9-18 - [i5]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
- [j7]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): 380 (2018) - [j6]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): 19:1-19:21 (2018) - [j5]Drahomira Herrmannova, Robert M. Patton, Petr Knoth, Christopher G. Stahl:
Do citations and readership identify seminal publications? Scientometrics 115(1): 239-262 (2018) - [c36]Drahomira Herrmannova, Steven Robert Young, Robert M. Patton, Christopher G. Stahl, Nicole C. Kleinstreuer, Mary S. Wolfe:
Unsupervised Identification of Study Descriptors in Toxicology Research: An Experimental Study. Louhi@EMNLP 2018: 71-82 - [c35]Drahomira Herrmannova, Petr Knoth, Christopher G. Stahl, Robert M. Patton, Jack C. Wells:
Research Collaboration Analysis Using Text and Graph Features. CICLing (2) 2018: 431-441 - [c34]Drahomira Herrmannova, Petr Knoth, Robert M. Patton:
Analyzing Citation-Distance Networks for Evaluating Publication Impact. LREC 2018 - [c33]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 - [c32]Randall Pittman, Hui Guan, Xipeng Shen, Seung-Hwan Lim, Robert M. Patton:
Exploring flexible communications for streamlining DNN ensemble training pipelines. SC 2018: 64:1-64:12 - [i4]Drahomira Herrmannova, Robert M. Patton, Petr Knoth, Christopher G. Stahl:
Do Citations and Readership Identify Seminal Publications? CoRR abs/1802.04853 (2018) - [i3]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 abs/1811.01183 (2018) - 2017
- [c31]Adam M. Terwilliger, Gabriel N. Perdue, David Isele, Robert M. Patton, Steven R. Young:
Vertex reconstruction of neutrino interactions using deep learning. IJCNN 2017: 2275-2281 - [c30]Robert M. Patton, Drahomira Herrmannova, Christopher G. Stahl, Jack C. Wells, Thomas E. Potok:
Audience Based View of Publication Impact. WOSP@JCDL 2017: 64-68 - [c29]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: 2:1-2:6 - [c28]J. Travis Johnston, Steven R. Young, David Hughes, Robert M. Patton, Devin White:
Optimizing Convolutional Neural Networks for Cloud Detection. MLHPC@SC 2017: 4:1-4:9 - [c27]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: 7:1-7:7 - [c26]Drahomira Herrmannova, Robert M. Patton, Petr Knoth, Christopher G. Stahl:
Citations and Readership are Poor Indicators of Research Excellence: Introducing TrueImpactDataset, a New Dataset for Validating Research Evaluation Metrics. SWM@WSDM 2017: 41-48 - [c25]Robert M. Patton, Thomas E. Potok, Petr Knoth, Drahomira Herrmannova:
Workshop on Scholarly Web Mining (SWM 2017). WSDM 2017: 819-820 - [i2]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 abs/1703.05364 (2017) - [i1]Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, James S. Plank:
A Survey of Neuromorphic Computing and Neural Networks in Hardware. CoRR abs/1705.06963 (2017) - 2016
- [j4]Robert M. Patton, Christopher G. Stahl, Jack C. Wells:
Measuring Scientific Impact Beyond Citation Counts. D Lib Mag. 22(9/10) (2016) - [c24]Stephen L. Smith, Stefano Cagnoni, Robert M. Patton:
MedGEC'16 Chairs' Welcome. GECCO (Companion) 2016: 1367 - [c23]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: 47-55 - 2015
- [c22]Katherine Senter, Sreenivas R. Sukumar, Robert M. Patton, Edward Chaum:
Using clinical data, hypothesis generation tools and PubMed trends to discover the association between diabetic retinopathy and antihypertensive drugs. IEEE BigData 2015: 2578-2582 - [c21]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: 4:1-4:5 - 2013
- [j3]Robert M. Patton, Christopher G. Stahl, Jayson B. Hines, Thomas E. Potok, Jack C. Wells:
Multi-year Content Analysis of User Facility Related Publications. D Lib Mag. 19(9/10) (2013) - [c20]Paul Logasa Bogen, Christopher T. Symons, Amber McKenzie, Robert M. Patton, Robert E. Gillen:
Massively scalable near duplicate detection in streams of documents using MDSH. IEEE BigData 2013: 480-486 - [c19]Robert M. Patton, Chad A. Steed, Christopher G. Stahl, Jim N. Treadwell:
Observing Community Resiliency in Social Media. ICCSA (5) 2013: 491-501 - 2012
- [j2]Robert M. Patton, Christopher G. Stahl, Thomas E. Potok, Jack C. Wells:
Identification of User Facility Related Publications. D Lib Mag. 18(7/8) (2012) - [c18]Robert M. Patton, Wade McNair, Christopher T. Symons, Jim N. Treadwell, Thomas E. Potok:
A Text Analysis Approach to Motivate Knowledge Sharing via Microsoft SharePoint. HICSS 2012: 3670-3678 - 2011
- [j1]Carlos C. Rojas, Robert M. Patton, Barbara G. Beckerman:
Characterizing Mammography Reports for Health Analytics. J. Medical Syst. 35(5): 1197-1210 (2011) - [c17]Justin M. Beaver, Robert M. Patton, Thomas E. Potok:
An approach to the automated determination of host information value. CICS 2011: 92-99 - [c16]Robert M. Patton, Justin M. Beaver, Thomas E. Potok:
Classification of Distributed Data Using Topic Modeling and Maximum Variation Sampling. HICSS 2011: 1-5 - [c15]Robert M. Patton, Carlos C. Rojas, Barbara G. Beckerman, Thomas E. Potok:
A Computational Framework for Search, Discovery, and Trending of Patient Health in Radiology Reports. HISB 2011: 104-111 - [c14]Robert M. Patton, Justin M. Beaver, Chad A. Steed, Thomas E. Potok, Jim N. Treadwell:
Hierarchical clustering and visualization of aggregate cyber data. IWCMC 2011: 1287-1291 - [p3]Robert M. Patton, Barbara G. Beckerman, Thomas E. Potok:
Analysis and Classification ofMammography Reports using Maximum Variation Sampling. Genetic and Evolutionary Computation: Medical Applications 2011 - 2010
- [c13]Robert M. Patton, Barbara G. Beckerman, Thomas E. Potok, Jim N. Treadwell:
Genetic algorithm for analysis of abdominal aortic aneurysms in radiology reports. GECCO (Companion) 2010: 1931-1936 - [c12]Robert M. Patton, Thomas E. Potok:
Discovering Potential Precursors of Mammography Abnormalities Based on Textual Features, Frequencies, and Sequences. ICAISC (1) 2010: 657-664 - [c11]Laura L. Pullum, Christopher T. Symons, Robert M. Patton, Barbara G. Beckerman:
Architecture-level dependability analysis of a medical decision support system. SEHC@ICSE 2010: 83-88 - [c10]Carlos C. Rojas, Robert M. Patton, Barbara G. Beckerman:
Characterizing mammography reports for health analytics. IHI 2010: 201-209
2000 – 2009
- 2009
- [c9]Robert M. Patton, Thomas E. Potok, Barbara G. Beckerman, Jim N. Treadwell:
A genetic algorithm for learning significant phrase patterns in radiology reports. GECCO (Companion) 2009: 2665-2670 - 2008
- [c8]Robert M. Patton, Jim N. Treadwell, Ryan A. Kerekes, Thomas E. Potok:
Discovery, analysis, and characteristics of event impacts. FUSION 2008: 1-8 - [c7]Robert M. Patton, Barbara G. Beckerman, Thomas E. Potok:
Analysis of mammography reports using maximum variation sampling. GECCO (Companion) 2008: 2061-2064 - [c6]Robert M. Patton, Thomas E. Potok:
Identifying Event Impacts by Monitoring the News Media. IV 2008: 333-337 - 2007
- [c5]Robert M. Patton, Thomas E. Potok:
Discovering event evidence amid massive, dynamic datasets. GECCO (Companion) 2007: 2895-2900 - [p2]Jesse St. Charles, Thomas E. Potok, Robert M. Patton, Xiaohui Cui:
Flocking-based Document Clustering on the Graphics Processing Unit. NICSO 2007: 27-37 - [p1]Xiaohui Cui, Robert M. Patton, Jim N. Treadwell, Thomas E. Potok:
Particle Swarm Based Collective Searching Model for Adaptive Environment. NICSO 2007: 211-220 - 2006
- [c4]Robert M. Patton, Thomas E. Potok:
Characterizing large text corpora using a maximum variation sampling genetic algorithm. GECCO 2006: 1877-1878 - 2005
- [c3]Paul J. Palathingal, Thomas E. Potok, Robert M. Patton:
Agent Based Approach for Searching, Mining and Managing Enormous Amounts of Spatial Image Data. FLAIRS 2005: 351-357 - 2004
- [c2]Robert M. Patton, Thomas E. Potok:
Adaptive Sampling of Text Documents. IASSE 2004: 42-45 - 2001
- [c1]Gwendolyn H. Walton, Robert M. Patton, Douglas J. Parsons:
Usage testing of military simulation systems. WSC 2001: 771-779
Coauthor Index
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