Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation

Keywords

Artificial neural network, Energy (signal processing), Nonlinear system, Backpropagation, Computer science

References

• The "independent components" of natural scenes are edge filters.Anthony J. Bell, Terrence J. Sejnowski — 1997
• What is Cognitive ScienceLee, Julia Ai Cheng — 1999
• Learning representations by back-propagating errorsDavid E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams — 1986
• Modelling the Statistics of Natural Images with Topographic Product of Student-t ModelsSimon Osindero — 2004
• Discovering Multiple Constraints that are Frequently Approximately SatisfiedGeoffrey E. Hinton, Yee Whye Teh — 2001
• Independent component filters of natural images compared with simple cells in primary visual cortexJ. H. van Hateren, A. van der Schaaf — 1998
• Training Products of Experts by Minimizing Contrastive DivergenceGeoffrey E. Hinton — 2002
• A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images.Aapo Hyvärinen, Patrik O. Hoyer — 2001
• Topographic Product Models Applied to Natural Scene StatisticsSimon Osindero, Max Welling, Geoffrey E. Hinton — 2006
• Emergence of simple-cell receptive field properties by learning a sparse code for natural imagesBruno A. Olshausen, David J. Field — 1996
• Backpropagation Through Time: What It Does and How to Do ItPaul J. Werbos — 1990
• Bayesian learning for neural networksRadford M. Neal — 1995

Cited by

• Deep Architectures for Baby AIYoshua Bengio — 2007
• Approximation and Relaxation Approaches for Parallel and Distributed Machine LearningStephen Tyree — 2014
• Learning generative models of mid-level structure in natural imagesNicolas Heess — 2012
• On the Expressive Efficiency of Sum Product NetworksJames Martens, Venkatesh Medabalimi — 2014
• Distributed representations for compositional semanticsKarl Moritz Hermann — 2014
• Learning techniques for multi-modal facial analysisMunawar Hayat — 2015
• Modeling Image Structure with Factorized Phase-Coupled Boltzmann MachinesCharles F. Cadieu, Kilian Koepsell — 2010
• Generative Modeling of Convolutional Neural NetworksJifeng Dai, Yang Lu, Yingnian Wu — 2014
• Automatic Image Annotation Using Convex Deep Learning ModelsNiharjyoti Sarangi, C. Chandra Sekhar — 2015
• Modeling pixel means and covariances using factorized third-order boltzmann machinesMarc’Aurelio Ranzato, Geoffrey E. Hinton — 2010
• Accurate estimation of large-scale IP traffic matrixDingde Jiang, Xingwei Wang, Lei Guo — 2011
• Spiking Deep Convolutional Neural Networks for Energy-Efficient Object RecognitionYongqiang Cao, Yang Chen, Deepak Khosla — 2015
• Learning Non-linear Reconstruction Models for Image Set ClassificationMunawar Hayat, Mohammed Bennamoun, Senjian An — 2014
• Learning Deep Architectures for AIYoshua Bengio — 2007
• Deep Reconstruction Models for Image Set ClassificationMunawar Hayat, Mohammed Bennamoun, Senjian An — 2015
• Neural Conditional Energy Models for Multi-label ClassificationHow Jing, Shou-De Lin — 2014
• Learning Generative Texture Models with extended Fields-of-ExpertsNicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton — 2009
• Computational models of location-invariant orthographic processingFrédéric Dandurand, Thomas Hannagan, Jonathan Grainger — 2013
• The Role of Syntax in Vector Space Models of Compositional SemanticsKarl Moritz Hermann, Phil Blunsom — 2013
• On the Representational Efficiency of Restricted Boltzmann MachinesJames Martens, Arkadev Chattopadhya, Toni Pitassi — 2013

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