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.
Kakade, Sham
Reinforcement learning and conditioning, mathematical models of neural processing.
Kali, Szabolcs
Learning and memory in the brain, hippocampus.
Kappen, Bert
Boltzmann machines, computational neurobiology, online learning.
Kearns, Michael
Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
Keysers, Daniel
Pattern recognition and statistical modelling for object recognition.
Koller, Daphne
Probabilistic models for complex uncertain domains.
Lafferty, John D.
Statistical machine learning, text and natural language processing, information retrieval, information theory.
Lawrence, Neil
Probabilistic models, variational methods.
Lawrence, Steve
Information dissemination and retrieval, machine learning and neural networks.
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