Modeling Documents with a Deep Boltzmann Machine Nitish Srivastava Ruslan Salakhutdinov Geoffrey Hinton

Source URL: www.cs.toronto.edu

• Artificial neural networks
• Statistics
• Applied mathematics
• Machine learning
• Multinomial logistic regression
• Restricted Boltzmann machine
• Boltzmann machine
• Latent Dirichlet allocation
• Logistic function
• Multimodal learning
• Deep learning

Where do features come from? Geoffrey Hinton Department of Computer Science, University of Toronto 6 King’s College Rd, M5S 3G4, Canada February 18, 2013

Source URL: www.cs.toronto.edu

• Artificial neural networks
• Restricted Boltzmann machine
• Boltzmann machine
• Autoencoder
• Backpropagation
• Feedforward neural network
• Generative model
• Connectionism
• Supervised learning
• Graphical model
• Machine learning
• Hidden Markov model

REGULARIZATION OF CONTEXT-DEPENDENT DEEP NEURAL NETWORKS WITH CONTEXT-INDEPENDENT MULTI-TASK TRAINING Peter Bell and Steve Renals Centre for Speech Technology Research, University of Edinburgh, Edinburgh EH8 9AB, UK ABST

Source URL: www.cstr.ed.ac.uk

• Artificial neural networks
• Machine learning
• Artificial intelligence
• Applied mathematics
• Speech recognition
• Boltzmann machine
• Recurrent neural network
• Multi-task learning
• Deep belief network
• Backpropagation
• Acoustic model
• Language model

Real-time Hebbian Learning from Autoencoder Features for Control Tasks To appear in: Proc. of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE 14). Cambridge, MA: MIT Press

Source URL: eplex.cs.ucf.edu

• Artificial neural networks
• Neuroscience
• Applied mathematics
• Cybernetics
• Autoencoder
• Hebbian theory
• Feature learning
• Deep learning
• Restricted Boltzmann machine
• Unsupervised learning
• Biological neural network
• Backpropagation

Curriculum Vitae Geoffrey E. Hinton July 14, 2015 Citizenship: Canadian (also British) Address:

Source URL: www.cs.toronto.edu

• Artificial neural networks
• Cognitive science
• Machine learning
• Academia
• Computational neuroscience
• Cybernetics
• Geoffrey Hinton
• Boltzmann machine
• Deep learning
• Speech recognition
• Artificial intelligence
• Helmholtz machine

Static Gesture Recognition with Restricted Boltzmann Machines Peter O’Donovan Department of Computer Science, University of Toronto 6 Kings College Rd, M5S 3G4, Canada

Source URL: www.dgp.toronto.edu

• Artificial intelligence
• Artificial neural networks
• Machine learning
• Statistics
• Learning
• Computational neuroscience
• Restricted Boltzmann machine
• Computational statistics
• Boltzmann machine
• Gesture recognition
• K-nearest neighbors algorithm
• Pattern recognition

The Shape Boltzmann Machine: a Strong Model of Object Shape S. M. Ali Eslami School of Informatics University of Edinburgh† Nicolas Heess

Source URL: arkitus.com

PDF Document

Source URL: www.neurotheory.columbia.edu

• Artificial neural networks
• Applied mathematics
• Cybernetics
• Learning
• Backpropagation
• Restricted Boltzmann machine
• Autoencoder
• Recurrent neural network
• Boltzmann machine
• Perceptron
• Supervised learning
• Deep learning

Parametric Herding Yutian Chen Max Welling Bren School of Information and Computer Science University of California, Irvine

Source URL: www.ics.uci.edu

• Machine learning
• Econometrics
• M-estimators
• Regression analysis
• Mixture model
• Maximum likelihood
• Boltzmann machine
• Expectation–maximization algorithm
• Vector quantization
• Statistics
• Estimation theory
• Statistical theory

Deep Belief Networks for phone recognition Abdel-rahman Mohamed, George Dahl, and Geoffrey Hinton Department of Computer Science University of Toronto {asamir,gdahl,hinton}@cs.toronto.edu

Source URL: www.cs.utoronto.ca

• Science
• Boltzmann machine
• Speech recognition
• Backpropagation
• Hidden Markov model
• Feedforward neural network
• Pattern recognition
• Markov chain
• DBN
• Neural networks
• Statistics
• Machine learning