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This page was last updated on October 6th, 2008
This page was last updated on October 6th, 2008
People (from Neural Networks)
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Zemel, Richard
Unsupervised learning, machine learning, computational models of neural processing.
Bengio, Samy
Torch machine learning library, including SVMTorch support vector machine program. Research on mixture models, hidden markov models, multimodal fusion, speaker verification.
McCallum, Andrew
Machine learning, text and information retrieval and extraction, reinforcement learning.
Allan, Moray
Computer vision, probabilistic models for image sequences, invariant features.
Lerner, Uri N.
Hybrid and Bayesian networks.
MacKay, David
Bayesian theory and inference, error-correcting codes, machine learning.
Hughes, Nicholas
Automated Analysis of ECG.
Xing, Eric
Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
Pathegama, Mahinda
Intelligent information systems, physiological sciences systems.
Attias, Hagai
Graphical models, variational Bayes, independent factor analysis.
Wu, Yingnian
Stochastic generative models for complex visual phenomena.
Tishby, Naftali
Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.
Pearlmutter, Barak
Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
Andrieu, Christophe
Particle filtering and Monte Carlo Markov Chain methods.
Schetinin, Vitaly
Biomedical data mining, diagnostic rule extraction and quality control in industry using a variety of techniques.
Saad, David
Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques.
Dayan , Peter
Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
Leen, Todd
Online learning, machine learning, learning dynamics.
Jensen, Finn Verner
Graphical models, belief propagation.
Wallis, Guy
Object recognition, cognitive neuroscience, interaction between vision and motor movements.
Joseph Wakeling's Neural Systems Research Page
Research papers and information on biologically inspired neural networks, brain modelling, AI and related topics. A cross-disciplinary site mixing information from physics, neuroscience, cognitive science and other fields.
Jordan, Michael I.
Graphical models, variational methods, machine learning, reasoning under uncertainty.
Bishop, Chris
Graphical models, variational methods, pattern recognition.
Morris, Quaid
Machine learning for medical diagnosis and biological data analysis.
Paccanaro, Alberto
Learning distributed representation of concepts from relational data.
Murray-Smith, Roderick
Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
Shuurmans, Dale
Computational learning, complex probability modelling.
Ng, Andrew
Reinforcement learning, machine learning.
Wiskott, Laurenz
Face recognition, Invariances in learning and vision.
Lawrence, Steve
Information dissemination and retrieval, machine learning and neural networks.
Saul, Lawrence K.
Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
Ghahramani, Zoubin
Sensorimotor control, unsupervised learning, probabilistic machine learning.
Hopfield, John J.
Neural networks, collective behaviour of systems of simple processors. Most noted for Hopfield networks.
Kali, Szabolcs
Learning and memory in the brain, hippocampus.
Storkey, Amos
Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.
Coolen, Ton
Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
Meila, Marina
Graphical models, learning in high dimensions, tree networks.
LeCun, Yann
Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
Shkolnik, Alexander
Neurally controlled robotics.
Williams, Christopher K. I.
Gaussian processes, image interpretation, graphical models, pattern recognition.
Opper, Manfred
Statistical physics, information theory and applied probability and applications to machien learning and complex systems.
Becker, Sue
Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.
Saund, Eric
Intermediate level structure in vision.
Dietterich, Thomas G.
Reinforcement learning, machine learning, supervised learning.
Simard, Patrice
Machine learning and generalization.
Rovetta, Stefano
Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.
Cottrell, Garrison W.
An artrificial intelligence researcher who is an expert on neural networks.
Kearns, Michael
Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
Murray, Alan
Neural networks and VLSI hardware.
Hinton, Geoffrey E.
Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.
Maass, Wolfgang
Theory of computation, computation in spiking neurons.
Honavar, Vasant
Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning.
Versace, Massimiliano
Neural networks applied to visual perception and computational modeling of mental disorders.
Olshausen, Bruno
Visual coding, statistics of images, independent components analysis.
Revow, Michael
Hand-written character recognition.
Wiegerinck, Wim
Inference in graphical models, mean field and variational approaches.
Roweis, Sam T.
Speech processing, auditory scene analysis, machine learning.
Tipping, Mike
Bayesian learning, relevance vector machine, probabilistic principal component analysis.
Chu, Selina
Artificial intelligence, machine learning, data mining.
Lafferty, John D.
Statistical machine learning, text and natural language processing, information retrieval, information theory.
Joshi, Prashant
Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.
Li, Zhaoping
Non-linear neural dynamics, visual segmentation, sensory processing.
Anthony, Martin
Computational learning theory, discrete mathematics.
Heskes, Tom
Learning and generalization in neural networks.
Zhu, Song Chun
Vision and graphics, statistical modelling and computing, neural computation.
Caruana, Rich
Multitask learning.
Amari, Shun-ichi
Neural network learning, information geometry.
de Freitas, Nando
Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
Sutton, Richard S.
Reinforcement learning.
Rasmussen, Carl Edward
Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.
Kappen, Bert
Boltzmann machines, computational neurobiology, online learning.
Teh, Yee Whye
Learning and inference in complex probabilistic models.
Koller, Daphne
Probabilistic models for complex uncertain domains.
Friedman, Nir
Learning of probabilistic models, applications to computational biology.
Adelson, Edward T.
Visual perception, machine vision, image processing.
Beal, Matthew J.
Bayesian inference, variational methods, graphical models.
Welling, Max
Unsupervised learning, probabilistic density estimation, machine vision.
Brown, Andrew
Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
Frohlich, Jochen
Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.
Calvin, William H.
Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
Mika, Sebastian
Machine learning and explorative data analysis: support vector machines, kernel principal component analysis and kernel Fisher discriminant analysis.
Sykacek, Peter
Brain Computer Interface.
Brody, Carlos D.
Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology.
de Garis, Hugo
Evolvable neural network models, neural networks for programmable hardware, large neural networks.
Dovzhenko, Alexander Yu.
Neural networks for computer clusters, oscillations in neural networks
Freeman, William T.
Bayesian perception, computer vision, image processing.
Beveridge, Ross
Computer vision, model-based object recognition, face recognition.
Leow, Wee Kheng
Computer vision, computational olfaction.
Schein, Andrew I.
Machine learning approaches to data mining focussing on text mining applications.
Lawrence, Neil
Probabilistic models, variational methods.
Minka, Thomas P.
Machine learning, computer vision, Bayesian methods.
Wunsch II, Donald C.
Reinforcement Learning, Adaptive Critic Designs, Control, Optimization, Graph Theory, Bioinformatics, Intrusion Detection.
Seung, Sebastian
Short-term memory, learning and memory in the brain, computational learning theory.
Neal, Radford
Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
Russell, Stuart
Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
Muresan, Raul C.
Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.
Roberts, Stephen
Machine learning and medical data analysis, independent component analysis and information theory.
Kawato, Mitsuo
Computational neuroscience, neural network modelling.
Sahani, Maneesh
Statistical analysis of neural data, experimental design in neuroscience.
Zhou, Zhi-Hua
Neural computing, data mining, evolutionary computing, ensemble networks.
Smola, Alex J.
Kernel methods for prediction and data analysis.
Jaakkola, Tommi S.
Graphical models, variational methods, kernel methods.
Bartlett, Marian Stewart
Image analysis with unsupervised learning, face recognition, facial expression analysis.
Andonie, Razvan
Data structures for computational intelligence.
Boutilier, Craig
Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
de Sa, Virginia
Supervised and unsupervised learning, cross-modal learning.
Cheung, Vincent
Machine learning and probabilistic graphical models for computer vision and computational molecular biology.
Rao, Rajesh P. N.
Models of human and computer vision.
Kakade, Sham
Reinforcement learning and conditioning, mathematical models of neural processing.
Wainwright, Martin
Statistical signal and image processing, natural image modelling, graphical models.
Frey, Brendan J.
Iterative decoding, unsupervised learning, graphical models.
Murphy, Kevin P.
Graphical models, machine learning, reinforcement learning.
Phillips, Jonathon
Face recognition.
Agakov, Felix
Probabilistic graphical modeling, statistical learning theory, pattern recognition, prediction, and causality.
Oja, Erkki
Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
Keysers, Daniel
Pattern recognition and statistical modelling for object recognition.
Bulsari, A.
Neural networks and nonlinear modelling for process engineering.
Yedidia, Jonathan S.
Statistical methods for inference and learning.
Sallans, Brian
Decision making under uncertainty, reinforcement learning, unsupervised learning.
Winther, Ole
Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.
Sejnowski, Terry
Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.
Weiss, Yair
Vision, Bayesian methods, neural computation.
Herbrich, Ralph
Statistical learning theory, support vector machines and kernel methods.
Garcia, Christophe
Computer vision, image analysis, neural networks.
Bach, Francis
Machine learning, kernel methods, kernel independent component analysis and graphical models
Fujita, Hajime
Partially observable markov decision processes (POMDP), reinforcement learning, multi-agent systems.
Ballard, Dana H.
Visual perception with neural networks.
Unsupervised learning, machine learning, computational models of neural processing.
Torch machine learning library, including SVMTorch support vector machine program. Research on mixture models, hidden markov models, multimodal fusion, speaker verification.
Machine learning, text and information retrieval and extraction, reinforcement learning.
Computer vision, probabilistic models for image sequences, invariant features.
Hybrid and Bayesian networks.
Bayesian theory and inference, error-correcting codes, machine learning.
Automated Analysis of ECG.
Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
Intelligent information systems, physiological sciences systems.
Graphical models, variational Bayes, independent factor analysis.
Stochastic generative models for complex visual phenomena.
Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.
Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
Particle filtering and Monte Carlo Markov Chain methods.
Biomedical data mining, diagnostic rule extraction and quality control in industry using a variety of techniques.
Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques.
Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
Online learning, machine learning, learning dynamics.
Graphical models, belief propagation.
Object recognition, cognitive neuroscience, interaction between vision and motor movements.
Research papers and information on biologically inspired neural networks, brain modelling, AI and related topics. A cross-disciplinary site mixing information from physics, neuroscience, cognitive science and other fields.
Graphical models, variational methods, machine learning, reasoning under uncertainty.
Graphical models, variational methods, pattern recognition.
Machine learning for medical diagnosis and biological data analysis.
Learning distributed representation of concepts from relational data.
Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
Computational learning, complex probability modelling.
Reinforcement learning, machine learning.
Face recognition, Invariances in learning and vision.
Information dissemination and retrieval, machine learning and neural networks.
Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
Sensorimotor control, unsupervised learning, probabilistic machine learning.
Neural networks, collective behaviour of systems of simple processors. Most noted for Hopfield networks.
Learning and memory in the brain, hippocampus.
Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.
Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
Graphical models, learning in high dimensions, tree networks.
Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
Neurally controlled robotics.
Gaussian processes, image interpretation, graphical models, pattern recognition.
Statistical physics, information theory and applied probability and applications to machien learning and complex systems.
Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.
Intermediate level structure in vision.
Reinforcement learning, machine learning, supervised learning.
Machine learning and generalization.
Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.
An artrificial intelligence researcher who is an expert on neural networks.
Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
Neural networks and VLSI hardware.
Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.
Theory of computation, computation in spiking neurons.
Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning.
Neural networks applied to visual perception and computational modeling of mental disorders.
Visual coding, statistics of images, independent components analysis.
Hand-written character recognition.
Inference in graphical models, mean field and variational approaches.
Speech processing, auditory scene analysis, machine learning.
Bayesian learning, relevance vector machine, probabilistic principal component analysis.
Artificial intelligence, machine learning, data mining.
Statistical machine learning, text and natural language processing, information retrieval, information theory.
Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.
Non-linear neural dynamics, visual segmentation, sensory processing.
Computational learning theory, discrete mathematics.
Learning and generalization in neural networks.
Vision and graphics, statistical modelling and computing, neural computation.
Multitask learning.
Neural network learning, information geometry.
Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
Reinforcement learning.
Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.
Boltzmann machines, computational neurobiology, online learning.
Learning and inference in complex probabilistic models.
Probabilistic models for complex uncertain domains.
Learning of probabilistic models, applications to computational biology.
Visual perception, machine vision, image processing.
Bayesian inference, variational methods, graphical models.
Unsupervised learning, probabilistic density estimation, machine vision.
Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.
Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
Machine learning and explorative data analysis: support vector machines, kernel principal component analysis and kernel Fisher discriminant analysis.
Brain Computer Interface.
Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology.
Evolvable neural network models, neural networks for programmable hardware, large neural networks.
Neural networks for computer clusters, oscillations in neural networks
Bayesian perception, computer vision, image processing.
Computer vision, model-based object recognition, face recognition.
Computer vision, computational olfaction.
Machine learning approaches to data mining focussing on text mining applications.
Probabilistic models, variational methods.
Machine learning, computer vision, Bayesian methods.
Reinforcement Learning, Adaptive Critic Designs, Control, Optimization, Graph Theory, Bioinformatics, Intrusion Detection.
Short-term memory, learning and memory in the brain, computational learning theory.
Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.
Machine learning and medical data analysis, independent component analysis and information theory.
Computational neuroscience, neural network modelling.
Statistical analysis of neural data, experimental design in neuroscience.
Neural computing, data mining, evolutionary computing, ensemble networks.
Kernel methods for prediction and data analysis.
Graphical models, variational methods, kernel methods.
Image analysis with unsupervised learning, face recognition, facial expression analysis.
Data structures for computational intelligence.
Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
Supervised and unsupervised learning, cross-modal learning.
Machine learning and probabilistic graphical models for computer vision and computational molecular biology.
Models of human and computer vision.
Reinforcement learning and conditioning, mathematical models of neural processing.
Statistical signal and image processing, natural image modelling, graphical models.
Iterative decoding, unsupervised learning, graphical models.
Graphical models, machine learning, reinforcement learning.
Face recognition.
Probabilistic graphical modeling, statistical learning theory, pattern recognition, prediction, and causality.
Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
Pattern recognition and statistical modelling for object recognition.
Neural networks and nonlinear modelling for process engineering.
Statistical methods for inference and learning.
Decision making under uncertainty, reinforcement learning, unsupervised learning.
Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.
Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.
Vision, Bayesian methods, neural computation.
Statistical learning theory, support vector machines and kernel methods.
Computer vision, image analysis, neural networks.
Machine learning, kernel methods, kernel independent component analysis and graphical models
Partially observable markov decision processes (POMDP), reinforcement learning, multi-agent systems.
Visual perception with neural networks.
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