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John W. Sheppard
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Publications
- 2024
- [i6]Giorgio Morales, John W. Sheppard:
Counterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones. CoRR abs/2403.10730 (2024) - 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) - [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 - [i5]Giorgio Morales, John W. Sheppard:
Counterfactual Explanations of Neural Network-Generated Response Curves. CoRR abs/2304.04063 (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) - [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 - [c80]Kyle Webster, John Sheppard:
Robust Spectral Based Compression of Hyperspectral Images using LSTM Autoencoders. IJCNN 2022: 1-8 - [i4]Giorgio Morales, John W. Sheppard:
Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation. CoRR abs/2212.06370 (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) - [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 - [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 - [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) - 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 - [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
- [j30]Logan Perreault, John W. Sheppard:
Compact structures for continuous time Bayesian networks. Int. J. Approx. Reason. 109: 19-41 (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 - [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 - [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 - [c60]Peter Lawson, Jordan Schupbach, Brittany Terese Fasy, John W. Sheppard:
Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images. Medical Imaging: Digital Pathology 2019: 109560G - 2018
- [j29]John W. Sheppard, Shane Strasser:
Multiple fault diagnosis using factored evolutionary algorithms. IEEE Instrum. Meas. Mag. 21(4): 27-38 (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 - 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) - [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
- [j24]Liessman Sturlaugson, John W. Sheppard:
Uncertain and negative evidence in continuous time Bayesian networks. Int. J. Approx. Reason. 70: 99-122 (2016) - [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 - [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
- [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) - [c40]Nathan Fortier, John W. Sheppard, Shane Strasser:
Parameter Estimation in Bayesian Networks Using Overlapping Swarm Intelligence. GECCO 2015: 9-16 - [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
- [c34]Liessman Sturlaugson, John W. Sheppard:
Factored Performance Functions with Structural Representation in Continuous Time Bayesian Networks. FLAIRS 2014 - [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
- [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
- [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) - [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 - [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
- [c17]Karthik Ganesan Pillai, John W. Sheppard:
Overlapping swarm intelligence for training artificial neural networks. SWIS 2011: 213-220 - 2009
- [j15]Stephyn G. W. Butcher, John W. Sheppard:
Distributional Smoothing in Bayesian Fault Diagnosis. IEEE Trans. Instrum. Meas. 58(2): 342-349 (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)
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last updated on 2024-04-25 01:36 CEST by the dblp team
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