People Neural Networks Artificial Intelligence

People Neural Networks Artificial Intelligence

People

Top: Computers: Artificial Intelligence: Neural Networks: People

Minsky, Marvin@

See Also:

- Supervised and unsupervised learning, cross-modal learning.
- Learning and inference in complex probabilistic models.
- Gesture recognition, Gaussian Process priors, control systems, probabilistic intelligent interfaces.
- 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.
- Graphical models, variational Bayes, independent factor analysis.
- Inference in graphical models, mean field and variational approaches.
- Bayesian inference, variational methods, graphical models.
- Brain Computer Interface.
- Statistical learning, machine learning approaches to computational biology, pattern recognition and control.
- Bayesian learning, relevance vector machine, probabilistic principal component analysis.
- Machine learning for medical diagnosis and biological data analysis.
- Data structures for computational intelligence.
- Reinforcement learning and conditioning, mathematical models of neural processing.
- An artrificial intelligence researcher who is an expert on neural networks.
- Speech processing, auditory scene analysis, machine learning.
- Graphical models, learning in high dimensions, tree networks.
- Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
- Learning and generalization in neural networks.
- Biomedical data mining, diagnostic rule extraction and quality control in industry using a variety of techniques.
- Reinforcement learning, probabilistic reasoning, machine learning, spoken dialogue systems.
- Visual perception with neural networks.
- Pattern recognition and statistical modelling for object recognition.
- Neural Networks, Spiking Neural Nets, Retinotopic Visual Architectures.
- Intelligent information systems, physiological sciences systems.
- Artificial intelligence, generative topographic map, missing data.
- Reinforcement learning.
- Statistical machine learning, text and natural language processing, information retrieval, information theory.
- Face recognition, Invariances in learning and vision.
- Computer vision, probabilistic models for image sequences, invariant features.
- Probabilistic graphical modeling, statistical learning theory, pattern recognition, prediction, and causality.
- Machine learning, pattern recognition, neural networks, voice processing, auditory computation.
- Physics of disordered systems. Working on dynamic replica theory for recurrent neural networks.
- Bayesian perception, computer vision, image processing.
- Learning and memory in the brain, hippocampus.
- Probabilistic models, variational methods.
- Theoretical neurophysiologist and author of The Cerebral Code, How Brains Think.
- Bayesian theory and inference, error-correcting codes, machine learning.
- Theory of computation, computation in spiking neurons.
- Machine learning approaches to data mining focussing on text mining applications.
- Unsupervised learning, probabilistic density estimation, machine vision.
- Variational algorithms for Gaussian processes, neural networks and support vector machines. Also work on belief propagation and protein structure prediction.
- Neural computing, data mining, evolutionary computing, ensemble networks.
- Probabilistic models for complex uncertain domains.
- Statistical analysis of neural data, experimental design in neuroscience.
- Decision making under uncertainty, reinforcement learning, unsupervised learning.
- Machine learning, computer vision, Bayesian methods.
- Computer vision, computational olfaction.
- Computer vision, model-based object recognition, face recognition.
- Many aspects of probabilistic modelling, identity uncertainty, expressive probability models.
- Graphical models, variational methods, kernel methods.
- Constructive learning, computational learning theory, spatial learning, cognitive modelling, incremental learning.
- Bayesian inference, Markov chain Monte Carlo simulation, machine learning.
- Sensorimotor control, unsupervised learning, probabilistic machine learning.
- Statistical physics, information theory and applied probability and applications to machien learning and complex systems.
- Non-linear neural dynamics, visual segmentation, sensory processing.
- Statistical signal and image processing, natural image modelling, graphical models.
- Neurally controlled robotics.
- Computational learning theory, discrete mathematics.
- Computational motor control, biologically realistic circuits, humanoid robots, spiking neurons.
- Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.
- Machine learning, kernel methods, kernel independent component analysis and graphical models
- Vision, Bayesian methods, neural computation.
- Overview of neural networks, and explanation of Java classes that implement backpropagation, and Kohonen feature maps.
- Reinforcement Learning, Adaptive Critic Designs, Control, Optimization, Graph Theory, Bioinformatics, Intrusion Detection.
- Neural networks and nonlinear modelling for process engineering.
- Graphical models, machine learning, reinforcement learning.
- Reinforcement learning, machine learning, supervised learning.
- Decision making and planning under uncertainty, reinforcement learning, game theory and economic models.
- Unsupervised learning with rich sensory input. Most noted for being a co-inventor of back-propagation.
- Research on Machine Learning/Neural Networks/Clustering. Applications to DNA microarray data analysis/industrial automation/information retrieval. Teaching activities.
- Representation and learning in neural processing systems, unsupervised learning, reinforcement learning.
- Neural networks and VLSI hardware.
- Unsupervised learning, PCA, ICA, SOM, statistical pattern recognition, image and signal analysis.
- Visual coding, statistics of images, independent components analysis.
- Stochastic generative models for complex visual phenomena.
- Neural networks, machine learning, acoustic source separation and localisation, independent component analysis, brain imaging.
- Sensory representation in visual cortex, memory representation and adaptive organization of visuo-motor transformations.
- Graphical models, variational methods, machine learning, reasoning under uncertainty.
- Unsupervised learning, machine learning, computational models of neural processing.
- Machine learning and explorative data analysis: support vector machines, kernel principal component analysis and kernel Fisher discriminant analysis.
- Graphical models, variational methods, pattern recognition.
- Belief networks, dynamic trees, image models, image processing, probabilistic methods in astronomy, scientific data mining, Gaussian processes and Hopfield neural networks.
- Evolvable neural network models, neural networks for programmable hardware, large neural networks.
- Graphical models, belief propagation.
- Machine learning, text and information retrieval and extraction, reinforcement learning.
- Computer vision, image analysis, neural networks.
- Learning of probabilistic models, applications to computational biology.
- Online learning, machine learning, learning dynamics.
- Intermediate level structure in vision.
- Iterative decoding, unsupervised learning, graphical models.
- Object recognition, cognitive neuroscience, interaction between vision and motor movements.
- Bayesian inference, Markov chain Monte Carlo methods, evaluation of learning methods, data compression.
- Torch machine learning library, including SVMTorch support vector machine program. Research on mixture models, hidden markov models, multimodal fusion, speaker verification.
- Kernel methods for prediction and data analysis.
- Machine learning and medical data analysis, independent component analysis and information theory.
- Particle filtering and Monte Carlo Markov Chain methods.
- Face recognition.
- Neural networks applied to visual perception and computational modeling of mental disorders.
- Statistical methods for inference and learning.
- Information dissemination and retrieval, machine learning and neural networks.
- Models of human and computer vision.
- Learning distributed representation of concepts from relational data.
- Gaussian processes, image interpretation, graphical models, pattern recognition.
- Visual perception, machine vision, image processing.
- Neural computing, error-correcting codes and cryptography using statistical and statistical mechanics techniques.
- Vision and graphics, statistical modelling and computing, neural computation.
- Computational neuroscience, neural network modelling.
- Short-term memory, learning and memory in the brain, computational learning theory.
- Automated Analysis of ECG.
- Handwritten recognition, convolutional networks, image compression. Noted for LeNet.
- Hybrid and Bayesian networks.
- Reinforcement learning, machine learning.
- Neural networks for sensor fusion, wireless sensor networks, software modeling, multimedia assets management architectures
- Computational learning, complex probability modelling.
- Somatosensory working memory, computation with action potentials, design of complex stimuli for sensory neurophysiology.
- Image analysis with unsupervised learning, face recognition, facial expression analysis.
- Boltzmann machines, computational neurobiology, online learning.
- Neural network learning, information geometry.
- Machine learning and generalization.
- Multitask learning.
- Machine learning and probabilistic graphical models for computer vision and computational molecular biology.
- Hand-written character recognition.
- Machine learning; applications to human-computer interaction, vision,neurophysiology, biology and cognitive science.
- Neural network models of learning and memory, computational neuroscience, unsupervised learning in perceptual systems.
- Partially observable markov decision processes (POMDP), reinforcement learning, multi-agent systems.
- Neural networks, collective behaviour of systems of simple processors. Most noted for Hopfield networks.
- Artificial intelligence, machine learning, data mining.
- Statistical learning theory, support vector machines and kernel methods.

MySQL - Cache Direct sec.