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How to Represent Part-Whole Hierarchies in a Neural Network

Neural computation, 2023-02, Vol.35 (3), p.413-452

Hinton, Geoffrey

2023

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Learning multiple layers of representation

Trends in cognitive sciences, 2007-10, Vol.11 (10), p.428-434

Hinton, Geoffrey E

2007

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An Efficient Learning Procedure for Deep Boltzmann Machines

Neural computation, 2012-08, Vol.24 (8), p.1967-2006

2012

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Application of Deep Belief Networks for Natural Language Understanding

IEEE/ACM transactions on audio, speech, and language processing, 2014-04, Vol.22 (4), p.778-784

2014

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Acoustic Modeling Using Deep Belief Networks

IEEE transactions on audio, speech, and language processing, 2012-01, Vol.20 (1), p.14-22

2012

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Deep Learning—A Technology With the Potential to Transform Health Care

JAMA : the journal of the American Medical Association, 2018-09, Vol.320 (11), p.1101

Hinton, Geoffrey

2018

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A Fast Learning Algorithm for Deep Belief Nets

Neural computation, 2006-07, Vol.18 (7), p.1527-1554

2006

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Training Products of Experts by Minimizing Contrastive Divergence

Neural computation, 2002-08, Vol.14 (8), p.1771-1800

Hinton, Geoffrey E.

2002

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Visualizing non-metric similarities in multiple maps

Machine learning, 2012-04, Vol.87 (1), p.33-55

2012

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New types of deep neural network learning for speech recognition and related applications: an overview

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, p.8599-8603

2013

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Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines

Neural computation, 2010-06, Vol.22 (6), p.1473-1492

2010

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Learning to represent visual input

Philosophical transactions of the Royal Society of London. Series B. Biological sciences, 2010-01, Vol.365 (1537), p.177-184

Hinton, Geoffrey E

2010

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Deep, Narrow Sigmoid Belief Networks Are Universal Approximators

Neural computation, 2008-11, Vol.20 (11), p.2629-2636

2008

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Learning a better representation of speech soundwaves using restricted boltzmann machines

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, p.5884-5887

2011

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Learning to Detect Roads in High-Resolution Aerial Images

Computer Vision – ECCV 2010, p.210-223

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To recognize shapes, first learn to generate images

Computational Neuroscience: Theoretical Insights into Brain Function, 2007, Vol.165, p.535-547

2007

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Modeling Natural Images Using Gated MRFs

IEEE transactions on pattern analysis and machine intelligence, 2013-09, Vol.35 (9), p.2206-2222

2013

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Where Do Features Come From?

Cognitive science, 2014-08, Vol.38 (6), p.1078-1101

Hinton, Geoffrey

2014

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Deep learning

Nature (London), 2015-05, Vol.521 (7553), p.436-444

2015

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Topographic Product Models Applied to Natural Scene Statistics

Neural computation, 2006-02, Vol.18 (2), p.381-414

2006

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Energy-Based Models for Sparse Overcomplete Representations

Journal of machine learning research, 2004-10, Vol.4 (7-8), p.1235-1260

2004

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A Practical Guide to Training Restricted Boltzmann Machines

Neural Networks: Tricks of the Trade, p.599-619

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Comparing Classification Methods for Longitudinal fMRI Studies

Neural computation, 2010-11, Vol.22 (11), p.2729-2762

2010

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Backpropagation and the brain

Nature reviews. Neuroscience, 2020-06, Vol.21 (6), p.335-346

2020

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A New Learning Algorithm for Mean Field Boltzmann Machines

Artificial Neural Networks — ICANN 2002, 2002, p.351-357

2002

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How to Represent Part-Whole Hierarchies in a Neural Network

Learning multiple layers of representation

An Efficient Learning Procedure for Deep Boltzmann Machines

Application of Deep Belief Networks for Natural Language Understanding

Acoustic Modeling Using Deep Belief Networks

Deep Learning—A Technology With the Potential to Transform Health Care

A Fast Learning Algorithm for Deep Belief Nets

Training Products of Experts by Minimizing Contrastive Divergence

Visualizing non-metric similarities in multiple maps

New types of deep neural network learning for speech recognition and related applications: an overview

Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines

Learning to represent visual input

Deep, Narrow Sigmoid Belief Networks Are Universal Approximators

Learning a better representation of speech soundwaves using restricted boltzmann machines

Learning to Detect Roads in High-Resolution Aerial Images

To recognize shapes, first learn to generate images

Modeling Natural Images Using Gated MRFs

Where Do Features Come From?

Deep learning

Topographic Product Models Applied to Natural Scene Statistics

Energy-Based Models for Sparse Overcomplete Representations

A Practical Guide to Training Restricted Boltzmann Machines

Comparing Classification Methods for Longitudinal fMRI Studies

Backpropagation and the brain

A New Learning Algorithm for Mean Field Boltzmann Machines

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