- Adelson, Edward T. Visual perception, machine vision, image processing.
- Allan, Moray Computer vision, probabilistic models for image sequences, invariant features.
- Amari, Shun-ichi Neural network learning, information geometry.
- Andonie, Razvan Data structures for computational intelligence.
- Andrieu, Christophe Particle filtering and Monte Carlo Markov Chain methods.
- Anthony, Martin Computational learning theory, discrete mathematics.
- Attias, Hagai Graphical models, variational Bayes, independent factor analysis.
- Bach, Francis Machine learning, kernel methods, kernel independent component analysis and graphical models
- Ballard, Dana H. Visual perception with neural networks.
- Beal, Matthew J. Bayesian inference, variational methods, graphical models, nonparametric Bayes.
- Becker, Sue Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.
- Beveridge, Ross Computer vision, model-based object recognition, face recognition.
- Bishop, Chris Graphical models, variational methods, pattern recognition.
- Bogaerts, Jan Blog about NLP using resonating neural networks.
- Boutilier, Craig Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
- Brown, Andrew Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
- Bulsari, A. Neural networks and nonlinear modelling for process engineering.
- Calvin, William H. Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
- Caruana, Rich Multitask learning.
- Cheung, Vincent Machine learning and probabilistic graphical models for computer vision and computational molecular biology.
- Chu, Selina Artificial intelligence, machine learning, data mining.
- Coolen, Ton Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
- Cottrell, Garrison W. An artificial intelligence researcher who is an expert on neural networks.
- Dahlem, Markus A. Neural network models of visual cortex to model neurological symptoms of migraine.
- Dayan , Peter Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
- de Freitas, Nando Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
- De vito, Saverio Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures
- De Wilde, Philippe Brain inspired models of uncertainty, linguistic and fuzzy uncertainty, uncertainty in dynamic multi-user environments.
- 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.
- Ghahramani, Zoubin Sensorimotor control, unsupervised learning, probabilistic machine learning.
- Grangier, David Research focusing on Machine Learning, Neural Networks, Kernel Machines, Computer Vision and Speech Processing.
- Hansen, Lars Kai Neural network ensembles, adaptive systems and applications in neuroinformatics.
- 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.
- Hughes, Nicholas Automated Analysis of ECG.
- Jaakkola, Tommi S. Graphical models, variational methods, kernel methods.
- Jordan, Michael I. Graphical models, variational methods, machine learning, reasoning under uncertainty.
- Joshi, Prashant Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.
- Kearns, Michael Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
- 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.
- LeCun, Yann Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
- Leen, Todd Online learning, machine learning, learning dynamics.
- Leow, Wee Kheng Computer vision, computational olfaction.
- Lerner, Uri N. Hybrid and Bayesian networks.
- Li, Zhaoping Non-linear neural dynamics, visual segmentation, sensory processing.
- Maass, Wolfgang Theory of computation, computation in spiking neurons.
- MacKay, David Bayesian theory and inference, error-correcting codes, machine learning.
- Malchiodi, Dario Machine learning, Learning from uncertain data.
- McCallum, Andrew Machine learning, text and information retrieval and extraction, reinforcement learning.
- Meila, Marina Graphical models, learning in high dimensions, tree networks.
- Minka, Thomas P. Machine learning, computer vision, Bayesian methods.
- Muresan, Raul C. Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.
- Murphy, Kevin P. Graphical models, machine learning, reinforcement learning.
- Murray, Alan Neural networks and VLSI hardware.
- Murray-Smith, Roderick Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
- Neal, Radford Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
- Oja, Erkki Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
- Olshausen, Bruno Visual coding, statistics of images, independent components analysis.
- Paccanaro, Alberto Learning distributed representation of concepts from relational data.
- Pearlmutter, Barak Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
- Peterson, Leif E. Researcher at Methodist Hospital Research Institute on classification technology and related fields.
- Prashant, Joshi Computational Neuroscientist. Research interests: reservoir computing, computational motor control, computation with spiking neurons.
- Rao, Rajesh P. N. Models of human and computer vision.
- Rasmussen, Carl Edward Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.
- Revow, Michael Hand-written character recognition.
- Roberts, Stephen Machine learning and medical data analysis, independent component analysis and information theory.
- Rovetta, Stefano Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.
- Roweis, Sam T. Speech processing, auditory scene analysis, machine learning.
- Russell, Stuart Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
- Rutkowski, Leszek Neural networks, fuzzy systems, computational intelligence.
- Sahani, Maneesh Statistical analysis of neural data, experimental design in neuroscience.
- Sallans, Brian Decision making under uncertainty, reinforcement learning, unsupervised learning.
- Saul, Lawrence K. Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
- Saund, Eric Intermediate level structure in vision.
- Sejnowski, Terry Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.
- Seung, Sebastian Short-term memory, learning and memory in the brain, computational learning theory.
- Shkolnik, Alexander Neurally controlled robotics.
- Storkey, Amos Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.
- Sykacek, Peter Brain Computer Interface.
- Teh, Yee Whye Learning and inference in complex probabilistic models.
- Tipping, Mike Varied machine learning and data analysis topics, including Bayesian inference, relevance vector machine, probabilistic principal component analysis and visualisation methods.
- Tishby, Naftali Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.
- Versace, Massimiliano Neural networks applied to visual perception and computational modeling of mental disorders.
- Wainwright, Martin Statistical signal and image processing, natural image modelling, graphical models.
- Wallis, Guy Object recognition, cognitive neuroscience, interaction between vision and motor movements.
- Weiss, Yair Vision, Bayesian methods, neural computation.
- Welling, Max Unsupervised learning, probabilistic density estimation, machine vision.
- Williams, Christopher K. I. Gaussian processes, image interpretation, graphical models, pattern recognition.
- Winther, Ole Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.
- Wiskott, Laurenz Face recognition, Invariances in learning and vision.
- Wu, Yingnian Stochastic generative models for complex visual phenomena.
- Xiaoguang, Rui Researcher at University of Science and Technology of China. About image annotation, image retrieval, social network analysis, pattern recognition and machine learning.
- Xing, Eric Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
- Zemel, Richard Unsupervised learning, machine learning, computational models of neural processing.
- Zhou, Zhi-Hua Neural computing, data mining, evolutionary computing, ensemble networks.

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**40** korisnika prisutno: 3 registrovanih, 1 skrivenih i 36 senkica. Ulogovani: cesko, **galeksic**, **niko650** (01:08)

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