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Minsky, Marvin
(12)
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Viewing in alphabetical order
Adelson, Edward T.
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http://web.mit.edu/persci/people/adelson/
Visual perception, machine vision, image processing.
Allan, Moray
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http://www.morayallan.com/
Computer vision, probabilistic models for image sequences, invariant features.
Amari, Shun-ichi
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http://www.brain.riken.jp/labs/mns/amari/home-E.html
Neural network learning, information geometry.
Andonie, Razvan
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http://www.cwu.edu/~andonie/
Data structures for computational intelligence.
Andrieu, Christophe
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http://www.stats.bris.ac.uk/~maxca/
Particle filtering and Monte Carlo Markov Chain methods.
Anthony, Martin
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http://www.maths.lse.ac.uk/Personal/martin/
Computational learning theory, discrete mathematics.
Attias, Hagai
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http://research.goldenmetallic.com/
Graphical models, variational Bayes, independent factor analysis.
Bach, Francis
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http://www.di.ens.fr/~fbach/
Machine learning, kernel methods, kernel independent component analysis and graphical models
Ballard, Dana H.
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http://www.cs.rochester.edu/users/faculty/dana
Visual perception with neural networks.
Bartlett, Marian Stewart
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http://ergo.ucsd.edu/~marni/
Image analysis with unsupervised learning, face recognition, facial expression analysis.
Beal, Matthew J.
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http://www.cse.buffalo.edu/faculty/mbeal
Bayesian inference, variational methods, graphical models, nonparametric Bayes.
Becker, Sue
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http://www.science.mcmaster.ca/Psychology/sb.html
Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.
Beveridge, Ross
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http://www.cs.colostate.edu/~ross/
Computer vision, model-based object recognition, face recognition.
Bishop, Chris
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http://research.microsoft.com/~cmbishop/
Graphical models, variational methods, pattern recognition.
Boutilier, Craig
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http://www.cs.toronto.edu/~cebly/
Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
Brody, Carlos D.
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http://www.cshl.edu/public/SCIENCE/brody.html
Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology.
Brown, Andrew
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http://www.ecs.soton.ac.uk/people/adb
Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
Bulsari, A.
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http://www.abo.fi/~abulsari
Neural networks and nonlinear modelling for process engineering.
Calvin, William H.
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http://faculty.washington.edu/wcalvin/
Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
Caruana, Rich
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http://www.cs.cmu.edu/~caruana/
Multitask learning.
Cheung, Vincent
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http://www.psi.toronto.edu/~vincent/
Machine learning and probabilistic graphical models for computer vision and computational molecular biology.
Chu, Selina
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http://www-scf.usc.edu/~selinach
Artificial intelligence, machine learning, data mining.
Coolen, Ton
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http://www.mth.kcl.ac.uk/~tcoolen/
Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
Cottrell, Garrison W.
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http://charlotte.ucsd.edu/~gary/
An artificial intelligence researcher who is an expert on neural networks.
Dahlem, Markus A.
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http://www.migraine-aura.org/EN/Markus_Dahlem.html
Neural network models of visual cortex to model neurological symptoms of migraine.
Dayan , Peter
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http://www.gatsby.ucl.ac.uk/~dayan/
Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
de Freitas, Nando
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http://www.cs.ubc.ca/~nando/
Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
de Garis, Hugo
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http://www.iss.whu.edu.cn/degaris/
Evolvable neural network models, neural networks for programmable hardware, large neural networks.
De vito, Saverio
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http://www.afs.enea.it/devito/
Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures
De Wilde, Philippe
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http://www.macs.hw.ac.uk/~pdw/
Brain inspired models of uncertainty, linguistic and fuzzy uncertainty, uncertainty in dynamic multi-user environments.
Dietterich, Thomas G.
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http://cs.oregonstate.edu/~tgd/
Reinforcement learning, machine learning, supervised learning.
Dr Hooman Shadnia
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http://www.shadnia.com
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.
Freeman, William T.
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http://people.csail.mit.edu/billf/wtf.html
Bayesian perception, computer vision, image processing.
Frey, Brendan J.
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http://www.psi.utoronto.ca/~frey/
Iterative decoding, unsupervised learning, graphical models.
Friedman, Nir
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http://www.cs.huji.ac.il/~nir/
Learning of probabilistic models, applications to computational biology.
Frohlich, Jochen
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http://rfhs8012.fh-regensburg.de/~saj39122/jfroehl/diplom/e-index.html
Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.
Garcia, Christophe
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http://www.csd.uoc.gr/~cgarcia
Computer vision, image analysis, neural networks.
Ghahramani, Zoubin
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http://www.gatsby.ucl.ac.uk/~zoubin
Sensorimotor control, unsupervised learning, probabilistic machine learning.
Hansen, Lars Kai
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http://eivind.imm.dtu.dk/staff/lkhansen/lkhansen.html
Neural network ensembles, adaptive systems and applications in neuroinformatics.
Herbrich, Ralph
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http://www.research.microsoft.com/users/rherb/
Statistical learning theory, support vector machines and kernel methods.
Heskes, Tom
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http://www.cs.ru.nl/~tomh/
Learning and generalization in neural networks.
Hinton, Geoffrey E.
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http://www.cs.toronto.edu/~hinton/
Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.
Honavar, Vasant
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http://www.cs.iastate.edu/~honavar/
Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning.
Hughes, Nicholas
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http://www.robots.ox.ac.uk/~nph/
Automated Analysis of ECG.
Jaakkola, Tommi S.
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http://www.ai.mit.edu/people/tommi
Graphical models, variational methods, kernel methods.
Jensen, Finn Verner
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http://www.cs.auc.dk/~fvj
Graphical models, belief propagation.
Jordan, Michael I.
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http://www.cs.berkeley.edu/~jordan/
Graphical models, variational methods, machine learning, reasoning under uncertainty.
Joshi, Prashant
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http://www.igi.tugraz.at/joshi
Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.
Kearns, Michael
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http://www.cis.upenn.edu/~mkearns/
Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
Koller, Daphne
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http://ai.stanford.edu/~koller/
Probabilistic models for complex uncertain domains.
Lafferty, John D.
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http://www.cs.cmu.edu/~lafferty/
Statistical machine learning, text and natural language processing, information retrieval, information theory.
Lawrence, Neil
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http://www.dcs.shef.ac.uk/~neil
Probabilistic models, variational methods.
Lawrence, Steve
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http://labs.google.com/people/lawrence/
Information dissemination and retrieval, machine learning and neural networks.
LeCun, Yann
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http://yann.lecun.com/
Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
Leen, Todd
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http://www.cse.ogi.edu/~tleen
Online learning, machine learning, learning dynamics.
Leow, Wee Kheng
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http://www.comp.nus.edu.sg/~leowwk
Computer vision, computational olfaction.
Lerner, Uri N.
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http://ai.stanford.edu/~uri/
Hybrid and Bayesian networks.
Li, Zhaoping
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http://www.gatsby.ucl.ac.uk/~zhaoping/
Non-linear neural dynamics, visual segmentation, sensory processing.
Maass, Wolfgang
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http://www.igi.tugraz.at/maass/
Theory of computation, computation in spiking neurons.
MacKay, David
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http://www.inference.phy.cam.ac.uk/mackay/
Bayesian theory and inference, error-correcting codes, machine learning.
Malchiodi, Dario
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http://homes.dsi.unimi.it/~malchiod/
Machine learning, Learning from uncertain data.
McCallum, Andrew
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http://www.cs.umass.edu/~mccallum/
Machine learning, text and information retrieval and extraction, reinforcement learning.
Meila, Marina
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http://www.stat.washington.edu/mmp/
Graphical models, learning in high dimensions, tree networks.
Minka, Thomas P.
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http://research.microsoft.com/~minka/
Machine learning, computer vision, Bayesian methods.
Muresan, Raul C.
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http://www.raulmuresan.home.ro/
Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.
Murphy, Kevin P.
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http://www.cs.berkeley.edu/~murphyk
Graphical models, machine learning, reinforcement learning.
Murray-Smith, Roderick
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http://www.dcs.gla.ac.uk/~rod/
Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
Murray, Alan
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http://www.ee.ed.ac.uk/~afm/
Neural networks and VLSI hardware.
Neal, Radford
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http://www.cs.toronto.edu/~radford
Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
Oja, Erkki
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http://www.cis.hut.fi/oja/
Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
Olier, Ivan
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http://www.lsi.upc.edu/~iaolier/
Artificial intelligence, generative topographic map, missing data.
Olshausen, Bruno
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https://redwood.berkeley.edu/bruno/
Visual coding, statistics of images, independent components analysis.
Opper, Manfred
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http://www.ncrg.aston.ac.uk/People/opperm/Welcome.html
Statistical physics, information theory and applied probability and applications to machine learning and complex systems.
Paccanaro, Alberto
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http://homes.gersteinlab.org/people/alberto/
Learning distributed representation of concepts from relational data.
Pearlmutter, Barak
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http://www-bcl.cs.may.ie/~barak/
Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
Rao, Rajesh P. N.
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http://www.cs.washington.edu/homes/rao/
Models of human and computer vision.
Rasmussen, Carl Edward
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http://learning.eng.cam.ac.uk/carl/
Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.
Revow, Michael
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http://www.cs.toronto.edu/~revow/
Hand-written character recognition.
Roberts, Stephen
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http://www.robots.ox.ac.uk/~sjrob/
Machine learning and medical data analysis, independent component analysis and information theory.
Rovetta, Stefano
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http://www.disi.unige.it/person/RovettaS/
Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.
Roweis, Sam T.
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http://www.cs.toronto.edu/~roweis/
Speech processing, auditory scene analysis, machine learning.
Russell, Stuart
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http://www.cs.berkeley.edu/~russell/
Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
Rutkowski, Leszek
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http://www.kik.pcz.czest.pl/~rutkowski/
Neural networks, fuzzy systems, computational intelligence.
Saad, David
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http://www.ncrg.aston.ac.uk/People/saadd/Welcome.html
Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques.
Sahani, Maneesh
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http://www.gatsby.ucl.ac.uk/~maneesh/
Statistical analysis of neural data, experimental design in neuroscience.
Sallans, Brian
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http://members.chello.at/hoebertz-sallans/sallans/index.html
Decision making under uncertainty, reinforcement learning, unsupervised learning.
Saul, Lawrence K.
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http://www.cs.ucsd.edu/~saul/
Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
Saund, Eric
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http://www2.parc.com/spl/members/saund/
Intermediate level structure in vision.
Schein, Andrew I.
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http://www.cis.upenn.edu/~ais
Machine learning approaches to data mining focussing on text mining applications.
Sejnowski, Terry
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http://www.salk.edu/faculty/faculty_details.php?id=48
Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.
Seung, Sebastian
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http://hebb.mit.edu/people/seung/
Short-term memory, learning and memory in the brain, computational learning theory.
Shkolnik, Alexander
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http://web.mit.edu/shkolnik/www/
Neurally controlled robotics.
Shuurmans, Dale
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http://www.lpaig.uwaterloo.ca/~dale/
Computational learning, complex probability modelling.
Storkey, Amos
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http://homepages.inf.ed.ac.uk/amos/
Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.
Sutton, Richard S.
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http://www-anw.cs.umass.edu/~rich/sutton.html
Reinforcement learning.
Sykacek, Peter
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http://www.robots.ox.ac.uk/~psyk/
Brain Computer Interface.
Teh, Yee Whye
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http://www.cs.utoronto.ca/~ywteh
Learning and inference in complex probabilistic models.
Tipping, Mike
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http://www.miketipping.com
Varied machine learning and data analysis topics, including Bayesian inference, relevance vector machine, probabilistic principal component analysis and visualisation methods.
Tishby, Naftali
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http://www.cs.huji.ac.il/~tishby/
Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.
Versace, Massimiliano
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http://www.maxversace.com
Neural networks applied to visual perception and computational modeling of mental disorders.
Wainwright, Martin
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http://www.eecs.berkeley.edu/~martinw/
Statistical signal and image processing, natural image modelling, graphical models.
Wallis, Guy
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http://www.uq.edu.au/~uqgwalli/
Object recognition, cognitive neuroscience, interaction between vision and motor movements.
Weiss, Yair
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http://www.cs.huji.ac.il/~yweiss/
Vision, Bayesian methods, neural computation.
Welling, Max
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http://www.cs.utoronto.ca/~welling
Unsupervised learning, probabilistic density estimation, machine vision.
Williams, Christopher K. I.
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http://www.dai.ed.ac.uk/homes/ckiw/
Gaussian processes, image interpretation, graphical models, pattern recognition.
Winther, Ole
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http://eivind.imm.dtu.dk/staff/winther/
Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.
Wiskott, Laurenz
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http://itb.biologie.hu-berlin.de/~wiskott/homepage.html
Face recognition, Invariances in learning and vision.
Wu, Yingnian
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http://www.stat.ucla.edu/~ywu/
Stochastic generative models for complex visual phenomena.
Xing, Eric
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http://www.cs.cmu.edu/~epxing/
Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
Yedidia, Jonathan S.
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http://www.merl.com/people/yedidia/
Statistical methods for inference and learning.
Zemel, Richard
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http://www.cs.utoronto.ca/~zemel/
Unsupervised learning, machine learning, computational models of neural processing.
Zhou, Zhi-Hua
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http://cs.nju.edu.cn/zhouzh/
Neural computing, data mining, evolutionary computing, ensemble networks.
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