Subcategories 1
Related categories 2
Sites 60
Visual perception, machine vision, image processing.
Computational learning theory, discrete mathematics.
Machine learning, kernel methods, kernel independent component analysis and graphical models
Bayesian inference, variational methods, graphical models, nonparametric Bayes.
Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.
Computer vision, model-based object recognition, face recognition.
Graphical models, variational methods, pattern recognition.
Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
Neural networks and nonlinear modelling for process engineering.
Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
Multitask learning.
Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures
Reinforcement learning, machine learning, supervised learning.
Bayesian perception, computer vision, image processing.
Learning of probabilistic models, applications to computational biology.
Research focusing on Machine Learning, Neural Networks, Kernel Machines, Computer Vision and Speech Processing.
Neural network ensembles, adaptive systems and applications in neuroinformatics.
Learning and generalization in neural networks.
Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.
Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning, causal inference, knowledge representation, preference reasoning.
Automated Analysis of ECG.
Graphical models, variational methods, machine learning, reasoning under uncertainty.
Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
Probabilistic models for complex uncertain domains.
Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
Computer vision, computational olfaction.
Hybrid and Bayesian networks.
Theory of computation, computation in spiking neurons.
Bayesian theory and inference, error-correcting codes, machine learning.
Machine learning, text and information retrieval and extraction, reinforcement learning.
Graphical models, learning in high dimensions, tree networks.
Machine learning, computer vision, Bayesian methods.
Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
Visual coding, statistics of images, independent components analysis.
Learning distributed representation of concepts from relational data.
Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
Models of human and computer vision.
Machine learning and medical data analysis, independent component analysis and information theory.
Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.
Speech processing, auditory scene analysis, machine learning.
Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
Neural networks, fuzzy systems, computational intelligence.
Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.
Varied machine learning and data analysis topics, including Bayesian inference, relevance vector machine, probabilistic principal component analysis and visualisation methods.
Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.
Neural networks applied to visual perception and computational modeling of mental disorders.
Statistical signal and image processing, natural image modelling, graphical models.
Vision, Bayesian methods, neural computation.
Gaussian processes, image interpretation, graphical models, pattern recognition.
Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.
Stochastic generative models for complex visual phenomena.
Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
Unsupervised learning, machine learning, computational models of neural processing.
Neural computing, data mining, evolutionary computing, ensemble networks.
Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.
Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.
Visual coding, statistics of images, independent components analysis.
Models of human and computer vision.
Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
Machine learning, computer vision, Bayesian methods.
Bayesian theory and inference, error-correcting codes, machine learning.
Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning, causal inference, knowledge representation, preference reasoning.
Visual perception, machine vision, image processing.
Speech processing, auditory scene analysis, machine learning.
Research focusing on Machine Learning, Neural Networks, Kernel Machines, Computer Vision and Speech Processing.
Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.
Machine learning, kernel methods, kernel independent component analysis and graphical models
Neural computing, data mining, evolutionary computing, ensemble networks.
Varied machine learning and data analysis topics, including Bayesian inference, relevance vector machine, probabilistic principal component analysis and visualisation methods.
Theory of computation, computation in spiking neurons.
Bayesian perception, computer vision, image processing.
Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.
Neural networks, fuzzy systems, computational intelligence.
Gaussian processes, image interpretation, graphical models, pattern recognition.
Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
Bayesian inference, variational methods, graphical models, nonparametric Bayes.
Unsupervised learning, machine learning, computational models of neural processing.
Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
Reinforcement learning, machine learning, supervised learning.
Neural network ensembles, adaptive systems and applications in neuroinformatics.
Graphical models, learning in high dimensions, tree networks.
Neural networks and nonlinear modelling for process engineering.
Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.
Graphical models, variational methods, pattern recognition.
Machine learning and medical data analysis, independent component analysis and information theory.
Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
Automated Analysis of ECG.
Computer vision, computational olfaction.
Statistical signal and image processing, natural image modelling, graphical models.
Learning of probabilistic models, applications to computational biology.
Hybrid and Bayesian networks.
Vision, Bayesian methods, neural computation.
Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures
Learning and generalization in neural networks.
Stochastic generative models for complex visual phenomena.
Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
Machine learning, text and information retrieval and extraction, reinforcement learning.
Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.
Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
Learning distributed representation of concepts from relational data.
Multitask learning.
Computational learning theory, discrete mathematics.
Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
Neural networks applied to visual perception and computational modeling of mental disorders.
Computer vision, model-based object recognition, face recognition.
Probabilistic models for complex uncertain domains.
Graphical models, variational methods, machine learning, reasoning under uncertainty.
