| Sue Becker - Computational neuroscience, neural network models of perceptual and cognitive processes including cortical and hippocampal memory systems, spatial memory, semantic memory organization, frontal executive control of memory.
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| Jordan, Michael I. - Models of visuomotor and other learning (Univ. of California, Berkeley, USA)
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| Sejnowski, Terry - Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.
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| Maass, Wolfgang - Theory of computation, computation in spiking neurons.
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| Neal, Radford - Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
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| Adelson, Edward H. - Human & machine vision (MIT, USA)
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| Brody, Carlos D. - Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology.
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| Peter Dayan - Builds mathematical and computational models of neural processing, with a particular emphasis on representation and learning. The main focus is on reinforcement learning and unsupervised learning, covering the ways that animals come to choose appropriate actions in the face of rewards and punishments, and the ways and goals of the process by which they come to form neural representations of the world. The models are informed and constrained by neurobiological, psychological and ethological data.
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| Amari, Shun-Ichi - Neural modeling (RIKEN Institute, Japan)
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| Ballard, Dana H. - Visual perception with neural networks.
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| Freeman, William T. - Bayesian perception, computer vision, image processing.
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| Ghahramani, Zoubin - Sensorimotor control, unsupervised learning, probabilistic machine learning.
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| Jaakkola, Tommi S. - Graphical models, variational methods, kernel methods.
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| Jensen, Finn Verner - Graphical models, belief propagation.
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| Murray, Alan - Neural networks and VLSI hardware.
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| Oja, Erkki - Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
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| Leen, Todd - Online learning, machine learning, learning dynamics.
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| Leow, Wee Kheng - Computer vision, computational olfaction.
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| Li, Zhaoping - Non-linear neural dynamics, visual segmentation, sensory processing.
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| Murphy, Kevin P. - Graphical models, machine learning, reinforcement learning.
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| Schetinin, Vitaly - Biomedical data mining, diagnostic rule extraction and quality control in industry using a variety of techniques.
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| Revow, Michael - Hand-written character recognition.
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| Maneesh Sahani - My research focuses on the statistical analysis of neural data and the design of experiments in neuroscience. The richness and density of information obtained from neural experiments is probably unrivalled in the history of experimental science. As such, new and creative methods are needed to collect sensible data and extract meaning from them.
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| Seung, Sebastian - Short-term memory, learning and memory in the brain, computational learning theory.
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| Bartlett, Marian Stewart - Image analysis with unsupervised learning, face recognition, facial expression analysis.
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| William H. Calvin's Books and Articles - Home page for William H. Calvin, theoretical neurophysiologist and author of The Cerebral Code, How the Brain Thinks.
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| Honavar, Vasant - Iowa State University. Machine learning, intelligent agents, information integration, probabilistic models, and bioinformatics and computational biology.
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| Sutton, Richard S. - Reinforcement learning.
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| Beveridge, Ross - Computer vision, model-based object recognition, face recognition.
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| de Sa, Virginia - Supervised and unsupervised learning, cross-modal learning.
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| Saad, David - Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques.
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| Teh, Yee Whye - Learning and inference in complex probabilistic models.
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| McCallum, Andrew - Machine learning, text and information retrieval and extraction, reinforcement learning.
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| Shuurmans, Dale - Computational learning, complex probability modelling.
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| Bruno Olshausen - The lab relates the function of the nervous system to the statistics of natural scenes. On his page he supplies scientific papers and software relating sparse coding.
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| Lafferty, John D. - Statistical machine learning, text and natural language processing, information retrieval, information theory.
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| Saund, Eric - Intermediate level structure in vision.
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| Ng, Andrew - Reinforcement learning, machine learning.
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| Zemel, Richard - Unsupervised learning, machine learning, computational models of neural processing.
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| Boutilier, Craig - Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
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| Pathegama, Mahinda - Intelligent information systems, physiological sciences systems.
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| Meila, Marina - University of Washington. Machine learning, probabilistic reasoning, graphical probability models, tree belief networks and mixtures of trees, maximum entropy discrimination, spectral clustering and image segmentation.
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| Caruana, Rich - Multitask learning.
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| Wiskott, Laurenz - Face recognition, Invariances in learning and vision.
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| Phillips, Jonathon - Face recognition.
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| Simard, Patrice - Machine learning and generalization.
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| Opper, Manfred - Statistical physics, information theory and applied probability and applications to machien learning and complex systems.
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| Yedidia, Jonathan S. - Statistical methods for inference and learning.
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| Zhu, Song Chun - Vision and graphics, statistical modelling and computing, neural computation.
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| Wu, Yingnian - Stochastic generative models for complex visual phenomena.
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| Rasmussen, Carl Edward - Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.
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| Lawrence, Steve - Information dissemination and retrieval, machine learning and neural networks.
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| Sallans, Brian - Decision making under uncertainty, reinforcement learning, unsupervised learning.
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| Brown, Andrew - Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
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| Paccanaro, Alberto - Learning distributed representation of concepts from relational data.
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| Morris, Quaid - Machine learning for medical diagnosis and biological data analysis.
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| Kakade, Sham - Reinforcement learning and conditioning, mathematical models of neural processing.
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| Kali, Szabolcs - Learning and memory in the brain, hippocampus.
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| Welling, Max - Unsupervised learning, probabilistic density estimation, machine vision.
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| Wallis, Guy - Object recognition, cognitive neuroscience, interaction between vision and motor movements.
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| Wunsch II, Donald C. - Reinforcement Learning, Adaptive Critic Designs, Control, Optimization, Graph Theory, Bioinformatics, Intrusion Detection.
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| Keysers, Daniel - Pattern recognition and statistical modelling for object recognition.
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| Rao, Rajesh P. N. - Models of human and computer vision.
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| Koller, Daphne - Probabilistic models for complex uncertain domains.
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| Lerner, Uri N. - Hybrid and Bayesian networks.
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| Tishby, Naftali - Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.
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| Rovetta, Stefano - Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.
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| de Freitas, Nando - Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
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| Saul, Lawrence K. - Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
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| LeCun, Yann - Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
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| Kearns, Michael - Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
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| Storkey, Amos - Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.
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| Sam Roweis - Machine Learning , Nonlinear Manifolds , Signal Processing , DNA Computing
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| Coolen, Ton - Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
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| Minka, Thomas P. - Machine learning, computer vision, Bayesian methods.
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| Bach, Francis - Machine learning, kernel methods, kernel independent component analysis and graphical models
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| Winther, Ole - Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.
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| Herbrich, Ralph - Statistical learning theory, support vector machines and kernel methods.
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| Roberts, Stephen - Machine learning and medical data analysis, independent component analysis and information theory.
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| Bishop, Chris - Graphical models, variational methods, pattern recognition.
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| Cottrell, Garrison W. - An artrificial intelligence researcher who is an expert on neural networks.
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| Frey, Brendan J. - Iterative decoding, unsupervised learning, graphical models.
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| Hinton, Geoffrey E. - Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.
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| David MacKay - University of Cambridge.
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| Smola, Alex J. - Kernel methods for prediction and data analysis.
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| Weiss, Yair - Vision, Bayesian methods, neural computation.
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| Williams, Christopher K. I. - Gaussian processes, image interpretation, graphical models, pattern recognition.
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| 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.
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| de Garis, Hugo - Evolvable neural network models, neural networks for programmable hardware, large neural networks.
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| Muresan, Raul C. - Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.
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| Friedman, Nir - Learning of probabilistic models, applications to computational biology.
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| Tipping, Mike - Bayesian learning, relevance vector machine, probabilistic principal component analysis.
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| Bengio, Samy - Torch machine learning library, including SVMTorch support vector machine program. Research on mixture models, hidden markov models, multimodal fusion, speaker verification.
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| Dietterich, Thomas G. - Reinforcement learning, machine learning, supervised learning.
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| Lawrence, Neil - Probabilistic models, variational methods.
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| Hopfield, John J. - Neural networks, collective behaviour of systems of simple processors. Most noted for Hopfield networks.
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| Russell, Stuart - Professor at the Computer Science Division of Berkeley University and author (with Peter Norvig) of the famous AI textbook "Artificial Intelligence: A Modern Approach".
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| Xing, Eric - Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
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| Mika, Sebastian - Machine learning and explorative data analysis: support vector machines, kernel principal component analysis and kernel Fisher discriminant analysis.
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| Murray-Smith, Roderick - Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
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| Sykacek, Peter - Brain Computer Interface.
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| Hughes, Nicholas - Automated Analysis of ECG.
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| Zhou, Zhi-Hua - Nanjing University. Machine learning, neural computing, data mining, pattern recognition and evolutionary computing. Online AI resources.
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| Wainwright, Martin - Statistical signal and image processing, natural image modelling, graphical models.
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| Beal, Matthew J. - Bayesian inference, variational methods, graphical models.
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| Bulsari, A. - Neural networks and nonlinear modelling for process engineering.
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| Agakov, Felix - Probabilistic graphical modeling, statistical learning theory, pattern recognition, prediction, and causality.
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| Andrieu, Christophe - Particle filtering and Monte Carlo Markov Chain methods.
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| Anthony, Martin - Computational learning theory, discrete mathematics.
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| Garcia, Christophe - Computer vision, image analysis, neural networks.
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| Versace, Massimiliano - Neural networks applied to visual perception and computational modeling of mental disorders.
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| Joshi, Prashant - Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.
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| Pearlmutter, Barak - Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
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| Fujita, Hajime - Partially observable markov decision processes (POMDP), reinforcement learning, multi-agent systems.
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| Chu, Selina - Artificial intelligence, machine learning, data mining.
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| Andrew Schein's Web Page - Machine learning approaches to data mining focussing on text mining applications
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| Frohlich, Jochen - Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.
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| Kawato, Mitsuo - Computational neuroscience, neural network modelling.
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| Attias, Hagai - Graphical models, variational Bayes, independent factor analysis.
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| Andonie, Razvan - Data structures for computational intelligence.
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| Allan, Moray - Computer vision, probabilistic models for image sequences, invariant features.
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| Shkolnik, Alexander - Neurally controlled robotics.
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| Wiegerinck, Wim - Inference in graphical models, mean field and variational approaches.
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| Kappen, Bert - Boltzmann machines, computational neurobiology, online learning.
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| Cheung, Vincent - Machine learning and probabilistic graphical models for computer vision and computational molecular biology.
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| Heskes, Tom - Learning and generalization in neural networks.
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| De vito, Saverio - Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures
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