An efficient way to learn deep generative models Geoffrey Hinton Canadian Institute for Advanced Research & Department of Computer Science

Source URL: www.cs.nyu.edu

Generating Text with Recurrent Neural Networks Ilya Sutskever James Martens Geoffrey Hinton University of Toronto, 6 King’s College Rd., Toronto, ON M5S 3G4 CANADA

Source URL: machinelearning.wustl.edu

Generating more realistic images using gated MRF’s Marc’Aurelio Ranzato Volodymyr Mnih Geoffrey E. Hinton Department of Computer Science

Source URL: papers.nips.cc

LEARNING A BETTER REPRESENTATION OF SPEECH SOUND WAVES USING RESTRICTED BOLTZMANN MACHINES Navdeep Jaitly, Geoffrey Hinton Department of Computer Science, University of Toronto, Toronto, M5S 3G4, Canada ABSTRACT State of

Source URL: www.cs.toronto.edu

Vocal Tract Length Perturbation for Speech Recognition with DNN-HMMs Navdeep Jaitly ● Geoffrey Hinton ●

Source URL: www.cs.toronto.edu

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

Network: Comput. Neural Syst–84. Printed in the UK PII: S0954-898XCascaded redundancy reduction Virginia R de Sa† and Geoffrey E Hinton

Source URL: www.cogsci.ucsd.edu

MLSS Tutorial on: Deep Belief Nets (An updated and extended version of my 2007 NIPS tutorial) Geoffrey Hinton Canadian Institute for Advanced Research

Source URL: mlg.eng.cam.ac.uk

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

Visualizing Similarity Data with a Mixture of Maps James Cook, Ilya Sutskever, Andriy Mnih and Geoffrey Hinton Department of Computer Science University of Toronto Toronto, Ontario M5S 3G4

Source URL: www.cs.toronto.edu