His scientific interests lie mostly in Artificial intelligence, Artificial neural network, Perception, Connectionism and Gradient descent. His Artificial intelligence research incorporates themes from Machine learning and Pattern recognition. As part of the same scientific family, he usually focuses on Machine learning, concentrating on Maximization and intersecting with Statistical model and Mixture model.
His Artificial neural network research is multidisciplinary, relying on both Expert system, Probability theory and Probability distribution. The study incorporates disciplines such as Method of steepest descent, Mathematical optimization and Heuristics in addition to Connectionism. In his study, Product of experts is inextricably linked to Semi-supervised learning, which falls within the broad field of Competitive learning.
His main research concerns Artificial intelligence, Perception, Communication, Machine learning and Cognitive psychology. His Artificial intelligence research includes themes of Computer vision and Pattern recognition. His Perception research includes elements of Matching, Speech recognition, Cognition and Set.
He works on Machine learning which deals in particular with Unsupervised learning. His study looks at the intersection of Cognitive psychology and topics like Visual perception with Visual learning. His Statistical model study incorporates themes from Mixture model and Supervised learning.
His primary areas of investigation include Artificial intelligence, Bayesian inference, Cognitive model, Probabilistic logic and Cognitive science. Robert A. Jacobs frequently studies issues relating to Pattern recognition and Artificial intelligence. Robert A. Jacobs studied Cognitive model and Modality that intersect with Stimulus modality and Categorization.
His Stimulus modality research is multidisciplinary, incorporating elements of Credence and Communication. As a part of the same scientific family, Robert A. Jacobs mostly works in the field of Probabilistic logic, focusing on Concept learning and, on occasion, Generative grammar, Context model and Visual learning. His Similarity study which covers Perception that intersects with Data compression, Artificial neural network, Deep neural networks, Human intelligence and Social psychology.
His primary areas of study are Communication, Sequence learning, Bayesian inference, Categorization and Modality. His work carried out in the field of Communication brings together such families of science as Object, Visual perception and Haptic technology. Robert A. Jacobs interconnects Credence, Cognitive science, Probabilistic logic and Cognitive model in the investigation of issues within Sequence learning.
His work deals with themes such as Categorical perception, Structural information theory, Form perception, Depth perception and Machine learning, which intersect with Bayesian inference. His studies deal with areas such as Artificial intelligence and Pattern recognition as well as Brain mapping. His research combines Visual short-term memory and Artificial intelligence.
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Adaptive mixtures of local experts
Robert A. Jacobs;Michael I. Jordan;Steven J. Nowlan;Geoffrey E. Hinton.
Neural Computation (1991)
Hierarchical mixtures of experts and the EM algorithm
Michael I. Jordan;Robert A. Jacobs.
Neural Computation (1994)
Increased Rates of Convergence Through Learning Rate Adaptation
Robert A. Jacobs.
Neural Networks (1987)
Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks
Robert A. Jacobs;Michael I. Jordan;Andrew G. Barto.
Cognitive Science (1991)
Bayesian integration of visual and auditory signals for spatial localization
Peter W. Battaglia;Robert A. Jacobs;Richard N. Aslin.
Journal of The Optical Society of America A-optics Image Science and Vision (2003)
Methods for combining experts' probability assessments
Robert A. Jacobs.
Neural Computation (1995)
Perception of speech reflects optimal use of probabilistic speech cues
Meghan Clayards;Michael K. Tanenhaus;Richard N. Aslin;Robert A. Jacobs.
Optimal integration of texture and motion cues to depth.
Robert A. Jacobs.
Vision Research (1999)
Comparing perceptual learning tasks: a review.
Ione Fine;Robert A. Jacobs.
Journal of Vision (2002)
Learning piecewise control strategies in a modular neural network architecture
R.A. Jacobs;M.I. Jordan.
systems man and cybernetics (1993)
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