Artificial Intelligence: Neural Networks: People: Page 2

Face recognition.

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

Statistical methods for inference and learning.

Stochastic generative models for complex visual phenomena.

Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.

Unsupervised learning, probabilistic density estimation, machine vision.

Object recognition, cognitive neuroscience, interaction between vision and motor movements.

Pattern recognition and statistical modelling for object recognition.

Models of human and computer vision.

Probabilistic models for complex uncertain domains.

Hybrid and Bayesian networks.

Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.

Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.

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

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

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

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

Speech processing, auditory scene analysis, machine learning.

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

Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.

Machine learning and medical data analysis, independent component analysis and information theory.

Graphical models, variational methods, pattern recognition.

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

Iterative decoding, unsupervised learning, graphical models.

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