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See also:
  • Adelson, Edward T. - Visual perception, machine vision, image processing.
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  • Allan, Moray - Computer vision, probabilistic models for image sequences, invariant features.
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  • Amari, Shun-ichi - Neural network learning, information geometry.
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  • Andonie, Razvan - Data structures for computational intelligence.
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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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  • Attias, Hagai - Graphical models, variational Bayes, independent factor analysis.
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  • Bach, Francis - Machine learning, kernel methods, kernel independent component analysis and graphical models
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  • Beal, Matthew J. - Bayesian inference, variational methods, graphical models, nonparametric Bayes.
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  • Becker, Sue - Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.
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  • Beveridge, Ross - Computer vision, model-based object recognition, face recognition.
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  • Bishop, Chris - Graphical models, variational methods, pattern recognition.
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  • Bogaerts, Jan - Blog about NLP using resonating neural networks.
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  • Boutilier, Craig - Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
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  • Brown, Andrew - Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
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  • Bulsari, A. - Neural networks and nonlinear modelling for process engineering.
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  • Calvin, William H. - Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
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  • Caruana, Rich - Multitask learning.
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  • Cheung, Vincent - Machine learning and probabilistic graphical models for computer vision and computational molecular biology.
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  • Coolen, Ton - Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
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  • Dayan , Peter - Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
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  • de Freitas, Nando - Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
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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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  • Dietterich, Thomas G. - Reinforcement learning, machine learning, supervised learning.
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  • Dr Hooman Shadnia - Dedicated to artificial neural networks and their applications in medical research and computational chemistry. Offers a quick tutorial on theory on ANNs written in Persian.
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  • Freeman, William T. - Bayesian perception, computer vision, image processing.
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  • Frey, Brendan J. - Iterative decoding, unsupervised learning, graphical models.
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  • Friedman, Nir - Learning of probabilistic models, applications to computational biology.
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  • Grangier, David - Research focusing on Machine Learning, Neural Networks, Kernel Machines, Computer Vision and Speech Processing.
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  • Hansen, Lars Kai - Neural network ensembles, adaptive systems and applications in neuroinformatics.
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  • Heskes, Tom - Learning and generalization in neural networks.
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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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  • Honavar, Vasant - Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning.
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  • Hughes, Nicholas - Automated Analysis of ECG.
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  • Jaakkola, Tommi S. - Graphical models, variational methods, kernel methods.
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  • Jordan, Michael I. - Graphical models, variational methods, machine learning, reasoning under uncertainty.
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  • Kearns, Michael - Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
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  • Koller, Daphne - Probabilistic models for complex uncertain domains.
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  • Lafferty, John D. - Statistical machine learning, text and natural language processing, information retrieval, information theory.
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  • LeCun, Yann - Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
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  • Leow, Wee Kheng - Computer vision, computational olfaction.
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  • Lerner, Uri N. - Hybrid and Bayesian networks.
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  • Li, Zhaoping - Non-linear neural dynamics, visual segmentation, sensory processing.
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  • Maass, Wolfgang - Theory of computation, computation in spiking neurons.
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  • MacKay, David - Bayesian theory and inference, error-correcting codes, machine learning.
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  • Malchiodi, Dario - Machine learning, Learning from uncertain data.
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  • McCallum, Andrew - Machine learning, text and information retrieval and extraction, reinforcement learning.
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  • Meila, Marina - Graphical models, learning in high dimensions, tree networks.
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  • Minka, Thomas P. - Machine learning, computer vision, Bayesian methods.
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  • Muresan, Raul C. - Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.
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  • Murphy, Kevin P. - Graphical models, machine learning, reinforcement learning.
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  • Murray-Smith, Roderick - Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
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  • Neal, Radford - Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
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  • Oja, Erkki - Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
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  • Olshausen, Bruno - Visual coding, statistics of images, independent components analysis.
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  • Paccanaro, Alberto - Learning distributed representation of concepts from relational data.
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  • Pearlmutter, Barak - Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
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  • Peterson, Leif E. - Researcher at Methodist Hospital Research Institute on classification technology and related fields.
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  • Prashant, Joshi - Computational Neuroscientist. Research interests: reservoir computing, computational motor control, computation with spiking neurons.
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  • Rao, Rajesh P. N. - Models of human and computer vision.
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  • Rasmussen, Carl Edward - Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.
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  • Revow, Michael - Hand-written character recognition.
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  • Roberts, Stephen - Machine learning and medical data analysis, independent component analysis and information theory.
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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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  • Roweis, Sam T. - Speech processing, auditory scene analysis, machine learning.
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  • Russell, Stuart - Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
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  • Rutkowski, Leszek - Neural networks, fuzzy systems, computational intelligence.
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  • Saul, Lawrence K. - Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
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  • Sejnowski, Terry - Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.
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  • Seung, Sebastian - Short-term memory, learning and memory in the brain, computational learning theory.
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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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  • Tipping, Mike - Varied machine learning and data analysis topics, including Bayesian inference, relevance vector machine, probabilistic principal component analysis and visualisation methods.
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  • Tishby, Naftali - Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.
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  • Versace, Massimiliano - Neural networks applied to visual perception and computational modeling of mental disorders.
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  • Wainwright, Martin - Statistical signal and image processing, natural image modelling, graphical models.
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  • Wallis, Guy - Object recognition, cognitive neuroscience, interaction between vision and motor movements.
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  • Weiss, Yair - Vision, Bayesian methods, neural computation.
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  • Welling, Max - Unsupervised learning, probabilistic density estimation, machine vision.
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  • Williams, Christopher K. I. - Gaussian processes, image interpretation, graphical models, pattern recognition.
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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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  • Wu, Yingnian - Stochastic generative models for complex visual phenomena.
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  • Xiaoguang, Rui - Researcher at University of Science and Technology of China. About image annotation, image retrieval, social network analysis, pattern recognition and machine learning.
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  • Xing, Eric - Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
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  • Zemel, Richard - Unsupervised learning, machine learning, computational models of neural processing.
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  • Zhou, Zhi-Hua - Neural computing, data mining, evolutionary computing, ensemble networks.
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Last update: November 9, 2014 at 12:24:08 UTC - edit