Search Results - "Sheppard, John W"

Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing by Morales, Giorgio, Sheppard, John W, Hegedus, Paul B, Maxwell, Bruce D

“…In recent years, the use of remotely sensed and on-ground observations of crop fields, in conjunction with machine learning techniques, has led to highly…”
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Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection by Morales, Giorgio, Sheppard, John W., Logan, Riley D., Shaw, Joseph A.

“…Hyperspectral imaging systems are becoming widely used due to their increasing accessibility and their ability to provide detailed spectral responses based on…”
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Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation by Morales, Giorgio, Sheppard, John W.

“…Accurate uncertainty quantification is necessary to enhance the reliability of deep learning (DL) models in real-world applications. In the case of regression…”
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Agent-Based Modeling in Electrical Energy Markets Using Dynamic Bayesian Networks by Dehghanpour, Kaveh, Nehrir, M. Hashem, Sheppard, John W., Kelly, Nathan C.

“…Due to uncertainties in generation and load, optimal decision making in electrical energy markets is a complicated and challenging task. Participating agents…”
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Uncertain and negative evidence in continuous time Bayesian networks by Sturlaugson, Liessman, Sheppard, John W.

“…The continuous time Bayesian network (CTBN) enables reasoning about complex systems by representing the system as a factored, finite-state, continuous-time…”
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Factored performance functions and decision making in continuous time Bayesian networks by Sturlaugson, Liessman, Perreault, Logan, Sheppard, John W.

“…The continuous time Bayesian network (CTBN) is a probabilistic graphical model that enables reasoning about complex, interdependent, and continuous-time…”
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Overlapping particle swarms for energy-efficient routing in sensor networks by Haberman, Brian K., Sheppard, John W.

“…Sensor networks are traditionally built using battery-powered, collaborative devices. These sensor nodes do not rely on dedicated infrastructure services…”
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Extensive transcriptomic study emphasizes importance of vesicular transport in C9orf72 expansion carriers by Dickson, Dennis W, Baker, Matthew C, Jackson, Jazmyne L, DeJesus-Hernandez, Mariely, Finch, NiCole A, Tian, Shulan, Heckman, Michael G, Pottier, Cyril, Gendron, Tania F, Murray, Melissa E, Ren, Yingxue, Reddy, Joseph S, Graff-Radford, Neill R, Boeve, Bradley F, Petersen, Ronald C, Knopman, David S, Josephs, Keith A, Petrucelli, Leonard, Oskarsson, Björn, Sheppard, John W, Asmann, Yan W, Rademakers, Rosa, van Blitterswijk, Marka

“…The majority of the clinico-pathological variability observed in patients harboring a repeat expansion in the C9orf72-SMCR8 complex subunit (C9orf72) remains…”
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Classification of Musical Timbre Using Bayesian Networks by Donnelly, Patrick J., Sheppard, John W.

“…In this article, we explore the use of Bayesian networks for identifying the timbre of musical instruments. Peak spectral amplitude in ten frequency windows is…”
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Evolving Intertask Mappings for Transfer in Reinforcement Learning by Hua, Minh, Sheppard, John W.

“…Recently, there has been a focus on using transfer learning to reduce the sample complexity in reinforcement learning. One component that enables transfer is…”
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Cross-Domain Similarity in Domain Adaptation for Human Activity Recognition by Kasim, Samra, Sheppard, John W.

“…Human Activity Recognition (HAR) is a difficult machine learning problem, even for state-of-the-art deep learning models, due to HAR data's within-domain and…”
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Tournament Topology Particle Swarm Optimization by Kuo, Jason, Sheppard, John W.

“…Particle swarm optimization (PSO) has become a popular algorithm for performing global numerical optimization; however, it is known that the topology of PSO…”
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Using Winning Lottery Tickets in Transfer Learning for Convolutional Neural Networks by Soelen, Ryan Van, Sheppard, John W.

“…Neural network pruning can be an effective method for creating more efficient networks without incurring a significant penalty in accuracy. It has been shown…”
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Efficient Convolutional Neural Networks for Multi-Spectral Image Classification by Senecal, Jacob J., Sheppard, John W., Shaw, Joseph A.

“…While a great deal of research has been directed towards developing neural network architectures for RGB images, there is a relative dearth of research…”
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Quantifying Uncertainty in Neural Network Ensembles using U-Statistics by Schupbach, Jordan, Sheppard, John W., Forrester, Tyler

“…Quantifying uncertainty is critically important to many applications of predictive modeling. In this paper we apply a recently developed method that uses…”
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Agent-Based Modeling of Retail Electrical Energy Markets With Demand Response by Dehghanpour, Kaveh, Nehrir, M. Hashem, Sheppard, John W., Kelly, Nathan C.

“…In this paper, we study the behavior of a day-ahead (DA) retail electrical energy market with price-based demand response from air conditioning (AC) loads…”
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Evaluating Explanations of Convolutional Neural Network Image Classifications by Shah, Sumeet S., Sheppard, John W.

“…In this paper, we seek to automate the evaluation of explanations of image classification decisions made by complex convolutional neural networks (CNN)…”
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Enhancing Neural Networks with Locality-Sensitive Clustering of Internal Representations by McAllister, Richard A., Sheppard, John W.

“…Some data exhibit natural divisions where the application of a single neural network leaves some accuracy on the table, thereby making a multi-network approach…”
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Demonstrating semantic interoperability of diagnostic reasoners via AI-ESTATE by Sheppard, John W, Butcher, Stephyn G W, Donnelly, Patrick J

“…The Institute for Electrical and Electronics Engineers (IEEE), through its Standards Coordinating Committee 20 (SCC20), is developing interface standards…”
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Colearning in Differential Games by Sheppard, John W

“…Game playing has been a popular problem area for research in artificial intelligence and machine learning for many years. In almost every study of game playing…”
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