Probabilistic graphical modeling, statistical learning theory, pattern recognition, prediction, and causality.

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

Visual perception with neural networks.

Image analysis with unsupervised learning, face recognition, facial expression analysis.

University at Buffalo, SUNY. Nonparamtric Bayes, bioinformatics, HMMs, probablisitic sensor fusion.

Torch machine learning library, including SVMTorch support vector machine program. Research on mixture models, hidden markov models, multimodal fusion, speaker verification.

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.

Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology.

Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.

Neural networks and nonlinear modelling for process engineering.

Multitask learning.

Machine learning and probabilistic graphical models for computer vision and computational molecular biology.

Artificial intelligence, machine learning, data mining.

Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.

An artrificial intelligence researcher who is an expert on neural networks.

Neural network models of visual cortex to model neurological symptoms of migraine.

Brain inspired models of uncertainty, linguistic and fuzzy uncertainty, uncertainty in dynamic multi-user environments.

Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures

Reinforcement learning, machine learning, supervised learning.

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.

Ed Adelson focuses on topics in human and machine vision, including mid-level vision, lightness perception, motion analysis, perceptual organization, and image data compression.

Bayesian perception, computer vision, image processing.

Iterative decoding, unsupervised learning, graphical models.

Learning of probabilistic models, applications to computational biology.

Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.

Computer vision, image analysis, neural networks.

Sensorimotor control, unsupervised learning, probabilistic machine learning.

Neural network ensembles, adaptive systems and applications in neuroinformatics.

Statistical learning theory, support vector machines and kernel methods.

Learning and generalization in neural networks.

Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.

Automated Analysis of ECG.

Graphical models, variational methods, kernel methods.

Graphical models, belief propagation.

Models of visuomotor and other learning (Univ. of California, Berkeley, USA)

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.

Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.

Reinforcement learning and conditioning, mathematical models of neural processing.

Learning and memory in the brain, hippocampus.

Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.

Probabilistic models for complex uncertain domains.

Statistical machine learning, text and natural language processing, information retrieval, information theory.

Probabilistic models, variational methods.

Information dissemination and retrieval, machine learning and neural networks.

Handwritten recognition, convolutional networks, image compression. Noted for LeNet.

Online learning, machine learning, learning dynamics.

Computer vision, computational olfaction.

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.

University of Washington. Machine learning, probabilistic reasoning, graphical probability models, tree belief networks and mixtures of trees, maximum entropy discrimination, spectral clustering and image segmentation.

Machine learning, computer vision, Bayesian methods.

Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.

Graphical models, machine learning, reinforcement learning.

Neural networks and VLSI hardware.

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.

Technical University of Catalonia. Unsupervised learning, probabilistic neural network, data mining.

Visual coding, statistics of images, independent components analysis.

Statistical physics, information theory and applied probability and applications to machine learning and complex systems.

Learning distributed representation of concepts from relational data.

Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.

Builds mathematical and computational models of neural processing, with a particular emphasis on representation and learning. The main focus is on reinforcement learning and unsupervised learning, covering the ways that animals come to choose appropriate actions in the face of rewards and punishments, and the ways and goals of the process by which they come to form neural representations of the world. The models are informed and constrained by neurobiological, psychological and ethological data.

Computational neuroscientist, with main areas of research interest being computational motor control, computational models of olfaction, computation with spiking neurons, neurocomputational basis of working memory and decision making, learning in biologically realistic circuits.

Models of human and computer vision.

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.

Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.

Neural networks, fuzzy systems, computational intelligence.

Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques.

Statistical analysis of neural data, experimental design in neuroscience.

Decision making under uncertainty, reinforcement learning, unsupervised learning.

Machine Learning , Nonlinear Manifolds , Signal Processing , DNA Computing

Machine learning, pattern recognition, neural networks, voice processing, auditory computation.

Intermediate level structure in vision.

Machine learning approaches to data mining focussing on text mining applications.

Biomedical data mining, diagnostic rule extraction and quality control in industry using a variety of techniques.

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.

Neurally controlled robotics.

Computational learning, complex probability modelling.

Machine learning and generalization.

Kernel methods for prediction and data analysis.

Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.

Computational neuroscience, neural network models of perceptual and cognitive processes including cortical and hippocampal memory systems, spatial memory, semantic memory organization, frontal executive control of memory.

Reinforcement learning.

Brain Computer Interface.

Learning and inference in complex probabilistic models.

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.

Artificial Intelligence Research Laboratory, Iowa State University.

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.

Inference in graphical models, mean field and variational approaches.

Home page for William H. Calvin, theoretical neurophysiologist and author of The Cerebral Code, How the Brain Thinks.

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.

Face recognition, Invariances in learning and vision.

Stochastic generative models for complex visual phenomena.

Statistical learning, machine learning approaches to computational biology, pattern recognition and control.

Statistical methods for inference and learning.

Unsupervised learning, machine learning, computational models of neural processing.

Neural computing, data mining, evolutionary computing, ensemble networks.

Bayesian inference, Markov chain Monte Carlo simulation, machine learning.

Evolvable neural network models, neural networks for programmable hardware, large neural networks.