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ORCIDJohn W. Sheppard
John Sheppard 0001
Person information
- affiliation: Montana State University, Bozeman, Montana, USA
Other persons with the same name
- John Sheppard 0002
![[0000-0003-4212-0329]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0003-4212-0329")
— Waterford Institute of Technology, Ireland (and 1 more) - John Sheppard 0003 — Yale School of Medicine, Department of Internal Medicine, New Haven, CT, USA
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Journal Articles
- 2025
[j35]Giorgio Morales, John W. Sheppard:
Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation. IEEE Trans. Neural Networks Learn. Syst. 36(2): 2843-2853 (2025)- 2023
- [j34]Jordan Schupbach, Elliott Pryor, Kyle Webster, John Sheppard:
A Risk-Based Approach to Prognostics and Health Management Combining Bayesian Networks and Continuous-Time Bayesian Networks. IEEE Instrum. Meas. Mag. 26(5): 3-11 (2023) - [j33]Giorgio Morales, John W. Sheppard, Paul B. Hegedus, Bruce D. Maxwell:
Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing. Sensors 23(1): 489 (2023) - 2022
- [j32]Md Asaduzzaman Noor, John W. Sheppard, Sean Yaw:
Mixing Grain to Improve Profitability in Winter Wheat Using Evolutionary Algorithms. SN Comput. Sci. 3(2): 172 (2022) - 2021
- [j31]Giorgio Morales, John W. Sheppard, Riley D. Logan, Joseph A. Shaw:
Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection. Remote. Sens. 13(18): 3649 (2021) - 2019
- [j30]Logan Perreault, John W. Sheppard:
Compact structures for continuous time Bayesian networks. Int. J. Approx. Reason. 109: 19-41 (2019) - 2018
- [j29]John W. Sheppard, Shane Strasser:
Multiple fault diagnosis using factored evolutionary algorithms. IEEE Instrum. Meas. Mag. 21(4): 27-38 (2018) - [j28]Kaveh Dehghanpour, M. Hashem Nehrir, John W. Sheppard, Nathan C. Kelly:
Agent-Based Modeling of Retail Electrical Energy Markets With Demand Response. IEEE Trans. Smart Grid 9(4): 3465-3475 (2018) - 2017
- [j27]Logan Perreault, Monica Thornton, John W. Sheppard, Joseph DeBruycker:
Disjunctive interaction in continuous time Bayesian networks. Int. J. Approx. Reason. 90: 253-271 (2017) - [j26]Liessman Sturlaugson, Logan Perreault, John W. Sheppard:
Factored performance functions and decision making in continuous time Bayesian networks. J. Appl. Log. 22: 28-45 (2017) - [j25]Shane Strasser, John W. Sheppard, Nathan Fortier, Rollie Goodman:
Factored Evolutionary Algorithms. IEEE Trans. Evol. Comput. 21(2): 281-293 (2017) - 2016
- [j24]Liessman Sturlaugson, John W. Sheppard:
Uncertain and negative evidence in continuous time Bayesian networks. Int. J. Approx. Reason. 70: 99-122 (2016) - 2015
- [j23]Houston King, Nathan Fortier, John W. Sheppard:
An AI-ESTATE conformant interface for net-centric diagnostic and prognostic reasoning. IEEE Instrum. Meas. Mag. 18(4): 18-24 (2015) - [j22]Liessman Sturlaugson, John W. Sheppard:
Sensitivity Analysis of Continuous Time Bayesian Network Reliability Models. SIAM/ASA J. Uncertain. Quantification 3(1): 346-369 (2015) - [j21]Nathan Fortier, John W. Sheppard, Shane Strasser:
Abductive inference in Bayesian networks using distributed overlapping swarm intelligence. Soft Comput. 19(4): 981-1001 (2015) - [j20]Caisheng Wang, Carol J. Miller, M. Hashem Nehrir, John W. Sheppard, Shawn P. McElmurry:
A load profile management integrated power dispatch using a Newton-like particle swarm optimization method. Sustain. Comput. Informatics Syst. 8: 8-17 (2015) - 2013
- [j19]Patrick J. Donnelly, John W. Sheppard:
Classification of Musical Timbre Using Bayesian Networks. Comput. Music. J. 37(4): 70-86 (2013) - 2012
- [j18]Jesse Berwald, Tomás Gedeon, John W. Sheppard:
Using machine learning to predict catastrophes in dynamical systems. J. Comput. Appl. Math. 236(9): 2235-2245 (2012) - [j17]Brian Haberman, John W. Sheppard:
Overlapping particle swarms for energy-efficient routing in sensor networks. Wirel. Networks 18(4): 351-363 (2012) - 2009
- [j16]John W. Sheppard:
Special Section on the 2007 IEEE AUTOTESTCON. IEEE Trans. Instrum. Meas. 58(2): 238-239 (2009) - [j15]Stephyn G. W. Butcher, John W. Sheppard:
Distributional Smoothing in Bayesian Fault Diagnosis. IEEE Trans. Instrum. Meas. 58(2): 342-349 (2009) - [j14]Kihoon Choi, Satnam Singh, Anuradha Kodali, Krishna R. Pattipati, John W. Sheppard, Setu Madhavi Namburu, Shunsuke Chigusa, Danil V. Prokhorov, Liu Qiao:
Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems. IEEE Trans. Instrum. Meas. 58(3): 602-611 (2009) - 2007
- [j13]John W. Sheppard, Stephyn G. W. Butcher:
A Formal Analysis of Fault Diagnosis with D-matrices. J. Electron. Test. 23(4): 309-322 (2007) - 2005
- [j12]John W. Sheppard, Mark A. Kaufman:
A Bayesian approach to diagnosis and prognosis using built-in test. IEEE Trans. Instrum. Meas. 54(3): 1003-1018 (2005) - 1998
- [j11]John W. Sheppard, William R. Simpson:
Managing Conflict in System Diagnosis. Computer 31(3): 69-76 (1998) - [j10]Lee A. Shombert, John W. Sheppard:
A Behavior Model for Next Generation Test Systems. J. Electron. Test. 13(3): 299-314 (1998) - [j9]John W. Sheppard:
Colearning in Differential Games. Mach. Learn. 33(2-3): 201-233 (1998) - 1997
- [j8]John W. Sheppard, Steven Salzberg:
A Teaching Strategy for Memory-Based Control. Artif. Intell. Rev. 11(1-5): 343-370 (1997) - 1996
- [j7]John W. Sheppard:
SCC20 attracts IEC participation. IEEE Des. Test Comput. 13(1): 2- (1996) - 1993
- [j6]William R. Simpson, John W. Sheppard:
Fault Isolation in an Integrated Diagnostic Environment. IEEE Des. Test Comput. 10(1): 52-66 (1993) - [j5]John W. Sheppard, William R. Simpson:
Performing Effective Fault Isolation in Integrated Diagnostics. IEEE Des. Test Comput. 10(2): 78-90 (1993) - 1992
- [j4]William R. Simpson, John W. Sheppard:
System Testability Assessment for Integrated Diagnostics. IEEE Des. Test Comput. 9(1): 40-54 (1992) - [j3]John W. Sheppard, William R. Simpson:
Applying Testability Analysis for Integrated Diagnostics. IEEE Des. Test Comput. 9(3): 65-78 (1992) - 1991
- [j2]William R. Simpson, John W. Sheppard:
System Complexity and Integrated Diagnostics. IEEE Des. Test Comput. 8(3): 16-30 (1991) - [j1]John W. Sheppard, William R. Simpson:
A Mathematical Model for Integrated Diagnostics. IEEE Des. Test Comput. 8(4): 25-38 (1991)
Conference and Workshop Papers
- 2025
- [c96]Giorgio Morales, John W. Sheppard:
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks. AAAI 2025: 19546-19553 - [c95]Nathan Patera, John Sheppard:
Analyzing the Effects of Memetic Variations on Convergence in Overlapping Swarm Intelligence. EvoApplications (2) 2025: 176-191 - 2024
- [c94]Matthew Green, John W. Sheppard:
On the Performance and Robustness of Linear Model U-Trees in Mimic Learning. ICMLA 2024: 152-159 - [c93]Md Asaduzzaman Noor, John W. Sheppard, Jason A. Clark:
Identifying Hierarchical Community Structures in Content-Based Scholarly Social Networks. ICMLA 2024: 440-447 - [c92]Giorgio Morales, John W. Sheppard:
Counterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones. IJCNN 2024: 1-8 - [c91]Giorgio Morales, John W. Sheppard:
Univariate Skeleton Prediction in Multivariate Systems Using Transformers. ECML/PKDD (8) 2024: 107-125 - [c90]Md Asaduzzaman Noor, Jason A. Clark, John W. Sheppard:
ScholarNodes: Applying Content-based Filtering to Recommend Interdisciplinary Communities within Scholarly Social Networks. SIGIR 2024: 2791-2795 - 2023
- [c89]Md Asaduzzaman Noor, John Sheppard, Jason A. Clark:
Finding Potential Research Collaborations from Social Networks Derived from Topic Models. BESC 2023: 1-7 - [c88]Minh Hua, John W. Sheppard:
Evolving Intertask Mappings for Transfer in Reinforcement Learning. CEC 2023: 1-8 - [c87]Kordel K. France, John W. Sheppard:
Factored Particle Swarm Optimization for Policy Co-training in Reinforcement Learning. GECCO 2023: 30-38 - [c86]Samra Kasim, John W. Sheppard:
Cross-Domain Similarity in Domain Adaptation for Human Activity Recognition. IJCNN 2023: 1-8 - [c85]Giorgio Morales, John W. Sheppard:
Counterfactual Explanations of Neural Network-Generated Response Curves. IJCNN 2023: 1-8 - [c84]Amy Peerlinck, John Sheppard:
Managing Objective Archives for Solution Set Reduction in Many-Objective Optimization. SSCI 2023: 1491-1496 - 2022
- [c83]Amy Peerlinck, John Sheppard:
Addressing Sustainability in Precision Agriculture via Multi-Objective Factored Evolutionary Algorithms. MIC 2022: 391-405 - [c82]Amy Peerlinck, John Sheppard:
Multi-Objective Factored Evolutionary Optimization and the Multi-Objective Knapsack Problem. CEC 2022: 1-8 - [c81]Scott Wahl, John Sheppard:
Approximate Orthogonal Spectral Autoencoders for Community Analysis in Social Networks. ICMLA 2022: 1159-1166 - [c80]Kyle Webster, John Sheppard:
Robust Spectral Based Compression of Hyperspectral Images using LSTM Autoencoders. IJCNN 2022: 1-8 - 2021
- [c79]Jason Kuo, John W. Sheppard:
Tournament Topology Particle Swarm Optimization. CEC 2021: 2265-2272 - [c78]Stephen Boisvert, John W. Sheppard:
Quality Diversity Genetic Programming for Learning Decision Tree Ensembles. EuroGP 2021: 3-18 - [c77]Md Asaduzzaman Noor, John W. Sheppard:
Evolutionary Grain-Mixing to Improve Profitability in Farming Winter Wheat. EvoApplications 2021: 113-129 - [c76]Giorgio Morales, John Sheppard, Riley D. Logan, Joseph A. Shaw:
Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks. IJCNN 2021: 1-8 - [c75]Farshina Nazrul Shimim, Mohammad Alali, M. Hashem Nehrir, John W. Sheppard, Maryam Bahramipanah, Zagros Shahooei:
Resiliency-Aware Power Management of Microgrids using Agent-based Dynamic Programming and Q-learning. ISGT Asia 2021: 1-5 - [c74]Elliott Pryor, Amy Peerlinck, John Sheppard:
A Study in Overlapping Factor Decomposition for Cooperative Co-Evolution. SSCI 2021: 1-8 - [c73]Na'Shea Wiesner, John Sheppard, Brian Haberman:
Using Particle Swarm Optimization to Learn a Lane Change Model for Autonomous Vehicle Merging. SSCI 2021: 1-8 - 2020
- [c72]Richard A. McAllister, John W. Sheppard:
Enhancing Neural Networks with Locality-Sensitive Clustering of Internal Representations. IJCNN 2020: 1-8 - [c71]Jordan Schupbach, John W. Sheppard, Tyler Forrester:
Quantifying Uncertainty in Neural Network Ensembles using U-Statistics. IJCNN 2020: 1-8 - [c70]Sumeet S. Shah, John W. Sheppard:
Evaluating Explanations of Convolutional Neural Network Image Classifications. IJCNN 2020: 1-8 - [c69]Na'Shea Wiesner, John Sheppard, Brian Haberman:
Autonomous Vehicle Control Using Particle Swarm optimization in a Mixed Control Environment. SSCI 2020: 2877-2884 - 2019
- [c68]Amy Peerlinck, John Sheppard, Julie Pastorino, Bruce D. Maxwell:
Optimal Design of Experiments for Precision Agriculture Using a Genetic Algorithm. CEC 2019: 1838-1845 - [c67]Benjamin Mitchell, John Sheppard:
Spatially Biased Random Forests. FLAIRS 2019: 20-25 - [c66]Neil S. Walton, John W. Sheppard, Joseph A. Shaw:
Using a genetic algorithm with histogram-based feature selection in hyperspectral image classification. GECCO 2019: 1364-1372 - [c65]Scott Wahl, John Sheppard, Elizabeth Shanahan:
Legislative Vote Prediction using Campaign Donations and Fuzzy Hierarchical Communities. ICMLA 2019: 718-725 - [c64]Richard McAllister, John Sheppard:
Exploring Transferability in Deep Neural Networks with Functional Data Analysis and Spatial Statistics. IJCNN 2019: 1-10 - [c63]Amy Peerlinck, John Sheppard, Jacob J. Senecal:
AdaBoost with Neural Networks for Yield and Protein Prediction in Precision Agriculture. IJCNN 2019: 1-8 - [c62]Jacob J. Senecal, John W. Sheppard, Joseph A. Shaw:
Efficient Convolutional Neural Networks for Multi-Spectral Image Classification. IJCNN 2019: 1-8 - [c61]Ryan Van Soelen, John W. Sheppard:
Using Winning Lottery Tickets in Transfer Learning for Convolutional Neural Networks. IJCNN 2019: 1-8 - [c60]Peter Lawson, Jordan Schupbach, Brittany Terese Fasy, John W. Sheppard:
Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images. Digital Pathology 2019: 109560G - 2018
- [c59]Stephyn G. W. Butcher, John W. Sheppard, Shane Strasser:
Pareto Improving Selection of the Global Best in Particle Swarm Optimization. CEC 2018: 1-8 - [c58]Richard McAllister, John Sheppard:
Evaluating Spatial Generalization of Stacked Autoencoders in Wind Vector Determination. FLAIRS 2018: 68-73 - [c57]Stephyn G. W. Butcher, John W. Sheppard, Shane Strasser:
Information sharing and conflict resolution in distributed factored evolutionary algorithms. GECCO 2018: 5-12 - [c56]Stephyn G. W. Butcher, John W. Sheppard, Brian K. Haberman:
Comparative performance and scaling of the pareto improving particle swarm optimization algorithm. GECCO (Companion) 2018: 83-84 - [c55]Stephyn G. W. Butcher, John W. Sheppard:
An actor model implementation of distributed factored evolutionary algorithms. GECCO (Companion) 2018: 1276-1283 - [c54]Scott Wahl, John Sheppard:
Association Rule Mining in Fuzzy Political Donor Communities. MLDM (2) 2018: 231-245 - 2017
- [c53]Scott Wahl, John Sheppard:
Fuzzy Spectral Hierarchical Communities in Evolving Political Contribution Networks. FLAIRS 2017: 371-376 - [c52]Shane Strasser, John W. Sheppard:
Convergence of Factored Evolutionary Algorithms. FOGA 2017: 81-94 - [c51]Richard McAllister, John Sheppard:
Deep learning for wind vector determination. SSCI 2017: 1-8 - [c50]Shane Strasser, John W. Sheppard:
Evaluating factored evolutionary algorithm performance on binary deceptive functions. SSCI 2017: 1-8 - [c49]Shane Strasser, John W. Sheppard, Stephyn Butcher:
A formal approach to deriving factored evolutionary algorithm architectures. SSCI 2017: 1-8 - 2016
- [c48]Shehzad Qureshi, John W. Sheppard:
Dynamic sampling in training artificial neural networks with overlapping swarm intelligence. CEC 2016: 440-446 - [c47]Logan Perreault, Shane Strasser, Monica Thornton, John W. Sheppard:
A Noisy-OR Model for Continuous Time Bayesian Networks. FLAIRS 2016: 668-673 - [c46]Stephyn Butcher, Shane Strasser, Jenna Hoole, Benjamin Demeo, John W. Sheppard:
Relaxing Consensus in Distributed Factored Evolutionary Algorithms. GECCO 2016: 5-12 - [c45]Shane Strasser, Rollie Goodman, John W. Sheppard, Stephyn Butcher:
A New Discrete Particle Swarm Optimization Algorithm. GECCO 2016: 53-60 - [c44]Hasari Tosun, Ben Mitchell, John W. Sheppard:
Assessing diffusion of spatial features in Deep Belief Networks. IJCNN 2016: 1625-1632 - [c43]Hasari Tosun, John W. Sheppard:
Fast classifier learning under bounded computational resources using Partitioned Restricted Boltzmann Machines. IJCNN 2016: 2894-2900 - [c42]Rollie Goodman, Monica Thornton, Shane Strasser, John W. Sheppard:
MICPSO: A method for incorporating dependencies into discrete particle swarm optimization. SSCI 2016: 1-8 - 2015
- [c41]Scott Wahl, John Sheppard:
Hierarchical Fuzzy Spectral Clustering in Social Networks using Spectral Characterization. FLAIRS 2015: 305-310 - [c40]Nathan Fortier, John W. Sheppard, Shane Strasser:
Parameter Estimation in Bayesian Networks Using Overlapping Swarm Intelligence. GECCO 2015: 9-16 - [c39]Patrick J. Donnelly, John W. Sheppard:
Cross-Dataset Validation of Feature Sets in Musical Instrument Classification. ICDM Workshops 2015: 94-101 - [c38]Ben Mitchell, Hasari Tosun, John W. Sheppard:
Deep learning using partitioned data vectors. IJCNN 2015: 1-8 - [c37]Logan Perreault, Monica Thornton, Rollie Goodman, John W. Sheppard:
A Swarm-Based Approach to Learning Phase-Type Distributions for Continuous Time Bayesian Networks. SSCI 2015: 1860-1867 - [c36]Liessman Sturlaugson, John W. Sheppard:
The Long-Run Behavior of Continuous Time Bayesian Networks. UAI 2015: 842-851 - 2014
- [c35]Patrick J. Donnelly, John W. Sheppard:
Clustering Spectral Filters for Extensible Feature Extraction in Musical Instrument Classification. FLAIRS 2014 - [c34]Liessman Sturlaugson, John W. Sheppard:
Factored Performance Functions with Structural Representation in Continuous Time Bayesian Networks. FLAIRS 2014 - [c33]Hasari Tosun, John W. Sheppard:
Training Restricted Boltzmann Machines with Overlapping Partitions. ECML/PKDD (3) 2014: 195-208 - [c32]Logan Perreault, Mike P. Wittie, John W. Sheppard:
Communication-aware distributed PSO for dynamic robotic search. SIS 2014: 65-72 - [c31]Nathan Fortier, John W. Sheppard, Shane Strasser:
Learning Bayesian classifiers using overlapping swarm intelligence. SIS 2014: 205-212 - [c30]Liessman Sturlaugson, John W. Sheppard:
Inference Complexity in Continuous Time Bayesian Networks. UAI 2014: 772-779 - 2013
- [c29]Rachel M. Green, John W. Sheppard:
Comparing Frequency- and Style-Based Features for Twitter Author Identification. FLAIRS 2013 - [c28]Tim Wylie, Michael A. Schuh, John W. Sheppard, Rafal A. Angryk:
Cluster Analysis for Optimal Indexing. FLAIRS 2013 - [c27]Liessman E. Sturlaugson, John W. Sheppard:
Principal component analysis preprocessing with Bayesian networks for battery capacity estimation. I2MTC 2013: 98-101 - [c26]Nathan Fortier, John W. Sheppard, Karthik Ganesan Pillai:
Bayesian abductive inference using overlapping swarm intelligence. SIS 2013: 263-270 - 2012
- [c25]Douglas E. Galarus, Rafal A. Angryk, John W. Sheppard:
Automated Weather Sensor Quality Control. FLAIRS 2012 - [c24]Michael A. Schuh, Rafal A. Angryk, John W. Sheppard:
Evolving Kernel Functions with Particle Swarms and Genetic Programming. FLAIRS 2012 - [c23]Ben Mitchell, John W. Sheppard:
Deep Structure Learning: Beyond Connectionist Approaches. ICMLA (1) 2012: 162-167 - [c22]Richard McAllister, John Sheppard:
Taxonomic Dimensionality Reduction in Bayesian Text Classification. ICMLA (1) 2012: 508-513 - [c21]Karthik Ganesan Pillai, John W. Sheppard:
Abductive inference in Bayesian belief networks using swarm intelligence. SCIS&ISIS 2012: 375-380 - [c20]Nathan Fortier, John W. Sheppard, Karthik Ganesan Pillai:
DOSI: Training artificial neural networks using overlapping swarm intelligence with local credit assignment. SCIS&ISIS 2012: 1420-1425 - 2011
- [c19]Patrick J. Donnelly, John W. Sheppard:
Evolving Four-Part Harmony Using Genetic Algorithms. EvoApplications (2) 2011: 273-282 - [c18]Hasari Tosun, John W. Sheppard:
Incorporating evidence into trust propagation models using Markov Random Fields. PerCom Workshops 2011: 263-269 - [c17]Karthik Ganesan Pillai, John W. Sheppard:
Overlapping swarm intelligence for training artificial neural networks. SWIS 2011: 213-220 - 2008
- [c16]Edward Kao, Peter VanMaasdam, John W. Sheppard:
Image-based tracking with Particle Swarms and Probabilistic Data Association. SIS 2008: 1-8 - 2007
- [c15]Sean R. Martin, Steve E. Wright, John W. Sheppard:
Offline and Online Evolutionary Bi-Directional RRT Algorithms for Efficient Re-Planning in Dynamic Environments. CASE 2007: 1131-1136 - 2004
- [c14]Brian Howard, John W. Sheppard:
The Royal Road Not Taken: A Re-examination of the Reasons for GA Failure on R1. GECCO (1) 2004: 1208-1219 - [c13]Rashad L. Moore, Ashley Williams, John W. Sheppard:
Multi-agent Simulation of Airline Travel Markets. GECCO (2) 2004: 1322-1323 - 1999
- [c12]Michael Waters, John Sheppard:
Genetic programming and co-evolution with exogenous fitness in an artificial life environment. CEC 1999: 1641-1650 - 1998
- [c11]William R. Simpson, John W. Sheppard:
Standard representations of diagnostic models. SMC 1998: 3032-3037 - 1997
- [c10]John W. Sheppard, Leslie A. Orlidge:
Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)-A New Standard for System Diagnostics. ITC 1997: 1020-1029 - 1996
- [c9]John W. Sheppard, William R. Simpson:
Improving the accuracy of diagnostics provided by fault dictionaries. VTS 1996: 180-185 - [c8]J. El-Ziq, Najmi T. Jarwala, Niraj K. Jha, Peter Marwedel, Christos A. Papachristou, Janusz Rajski, John W. Sheppard:
Hardware-Software Co-Design for Test: It's the Last Straw! VTS 1996: 506-507 - 1995
- [c7]John W. Sheppard, Steven Salzberg:
Combining Genetic Algorithms with Memory Based Reasoning. ICGA 1995: 452-459 - 1993
- [c6]William R. Simpson, John W. Sheppard:
The Impact of Commercial Off-The-Shelf (COTS) Equipment on System Test and Diagnosis. ITC 1993: 30-36 - [c5]John W. Sheppard:
Testing Fully Testable Systems: A Case Study. ITC 1993: 268 - 1992
- [c4]William R. Simpson, John W. Sheppard:
System Perspective on Diagnostic Testing. ITC 1992: 547 - 1991
- [c3]William R. Simpson, John W. Sheppard:
An Intelligent Approach to Automatic Test Equipment. ITC 1991: 419-425 - 1990
- [c2]John W. Sheppard, William R. Simpson:
Using a Competitive Learning Neural Network to Evaluate Software Complexity. SIGSMALL/PC Symposium 1990: 262-267 - 1988
- [c1]John W. Sheppard, William R. Simpson:
Functional path analysis: an approach to software verification. ACM Conference on Computer Science 1988: 266-272
Informal and Other Publications
- 2025
- [i9]Seyed Mojtaba Mohasel, John Sheppard, Lindsey K. Molina, Richard R. Neptune, Shane R. Wurdeman, Corey A. Pew:
MicroNAS: An Automated Framework for Developing a Fall Detection System. CoRR abs/2504.07397 (2025) - 2024
- [i8]Giorgio Morales, John W. Sheppard:
Counterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones. CoRR abs/2403.10730 (2024) - [i7]Giorgio Morales, John W. Sheppard:
Univariate Skeleton Prediction in Multivariate Systems Using Transformers. CoRR abs/2406.17834 (2024) - [i6]Giorgio Morales, John W. Sheppard:
Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks. CoRR abs/2412.10570 (2024) - 2023
- [i5]Giorgio Morales, John W. Sheppard:
Counterfactual Explanations of Neural Network-Generated Response Curves. CoRR abs/2304.04063 (2023) - 2022
- [i4]Giorgio Morales, John W. Sheppard:
Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation. CoRR abs/2212.06370 (2022) - 2021
- [i3]Giorgio Morales, John W. Sheppard, Riley D. Logan, Joseph A. Shaw:
Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks. CoRR abs/2106.00645 (2021) - [i2]Giorgio Morales, John W. Sheppard:
Two-dimensional Deep Regression for Early Yield Prediction of Winter Wheat. CoRR abs/2111.08069 (2021) - [i1]Md Asaduzzaman Noor, Sean Yaw, Binhai Zhu, John W. Sheppard:
Optimal Grain Mixing is NP-Complete. CoRR abs/2112.08501 (2021)
Coauthor Index
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