Dietterich, Thomas G.
Reinforcement learning, machine learning, supervised learning.
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.
Freeman, William T.
Bayesian perception, computer vision, image processing.
Frey, Brendan J.
Iterative decoding, unsupervised learning, graphical models.
Friedman, Nir
Learning of probabilistic models, applications to computational biology.
Frohlich, Jochen
Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.
Fujita, Hajime
Partially observable markov decision processes (POMDP), reinforcement learning, multi-agent systems.
Garcia, Christophe
Computer vision, image analysis, neural networks.
Ghahramani, Zoubin
Sensorimotor control, unsupervised learning, probabilistic machine learning.
Herbrich, Ralph
Statistical learning theory, support vector machines and kernel methods.
Heskes, Tom
Learning and generalization in neural networks.
Hinton, Geoffrey E.
Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.
Honavar, Vasant
Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning.
Hopfield, John J.
Neural networks, collective behaviour of systems of simple processors. Most noted for Hopfield networks.
Hughes, Nicholas
Automated Analysis of ECG.
Jaakkola, Tommi S.
Graphical models, variational methods, kernel methods.
Jensen, Finn Verner
Graphical models, belief propagation.
Jordan, Michael I.
Graphical models, variational methods, machine learning, reasoning under uncertainty.
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.
Joshi, Prashant
Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.
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