Related categories 2
Adelson, Edward T.
Visual perception, machine vision, image processing.
Computer vision, probabilistic models for image sequences, invariant features.
Neural network learning, information geometry.
Data structures for computational intelligence.
Particle filtering and Monte Carlo Markov Chain methods.
Computational learning theory, discrete mathematics.
Machine learning, kernel methods, kernel independent component analysis and graphical models
Beal, Matthew J.
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.
Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
Neural networks and nonlinear modelling for process engineering.
Calvin, William H.
Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
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
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.
Learning of probabilistic models, applications to computational biology.
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.
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.
Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning, causal inference, knowledge representation, preference reasoning.
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.
Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
Probabilistic models for complex uncertain domains.
Lafferty, John D.
Statistical machine learning, text and natural language processing, information retrieval, information theory.
Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
Leow, Wee Kheng
Computer vision, computational olfaction.
Lerner, Uri N.
Hybrid and Bayesian networks.
Non-linear neural dynamics, visual segmentation, sensory processing.
Theory of computation, computation in spiking neurons.
Bayesian theory and inference, error-correcting codes, machine learning.
Machine learning, Learning from uncertain data.
Machine learning, text and information retrieval and extraction, reinforcement learning.
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.
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.
Peterson, Leif E.
Researcher at Methodist Hospital Research Institute on classification technology and related fields.
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.
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.
Roweis, Sam T.
Speech processing, auditory scene analysis, machine learning.
Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
Neural networks, fuzzy systems, computational intelligence.
Saul, Lawrence K.
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.
Object recognition, cognitive neuroscience, interaction between vision and motor movements.
Vision, Bayesian methods, neural computation.
Williams, Christopher K. I.
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.
Researcher at University of Science and Technology of China. About image annotation, image retrieval, social network analysis, pattern recognition and machine learning.
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.
Last update:May 17, 2016 at 21:33:03 UTC