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Computers: Artificial Intelligence: People
Science: Social Sciences: Psychology: Cognitive: People
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.
- Graphical models, variational Bayes, independent factor analysis.
- 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.
- Blog about NLP using resonating neural networks.
- 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.
- Multitask learning.
- Machine learning and probabilistic graphical models for computer vision and computational molecular biology.
- 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.
- 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.
- Hand-written character recognition.
- 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.
- Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.
- Short-term memory, learning and memory in the brain, computational learning theory.
- 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.
- Unsupervised learning, probabilistic density estimation, machine vision.
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.
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