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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 27 1 Deep Neural Network Acoustic Models for ASR Mohamed, Abdel-rahman. - 2014 BASE Show details 2 Acoustic modeling using deep belief networks Mohamed, Abdel-rahman; Dahl, George E.; Hinton, Geoffrey In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 20 (2012) 1, 14-22 BLLDB OLC Linguistik Show details 3 Discovering binary codes for documents by learning deep generative models Hinton, Geoffrey; Salakhutdinov, Ruslan In: Topics in cognitive science. - Hoboken, NJ [u.a.] : Wiley 3 (2011) 1, 74-91 BLLDB OLC Linguistik Show details 4 Learning Deep Generative Models Salakhutdinov, Ruslan. - 2009 Abstract: Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing. The aim of the thesis is to demonstrate that deep generative models that contain many layers of latent variables and millions of parameters can be learned efficiently, and that the learned high-level feature representations can be successfully applied in a wide spectrum of application domains, including visual object recognition, information retrieval, and classification and regression tasks. In addition, similar methods can be used for nonlinear dimensionality reduction. Keyword: computer science; probabilistic graphical models URL: http://hdl.handle.net/1807/19226 BASE Hide details 5 Unsupervised discovery of nonlinear structure using contrastive backpropagation Welling, Max; Hinton, Geoffrey; Teh, Yee-Whye... In: Cognitive science. - Hoboken, NJ : Wiley-Blackwell 30 (2006) 4, 725-731 BLLDB OLC Linguistik Show details 6 Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation Hinton, Geoffrey; Osindero, Simon; Welling, Max... In: Cognitive science. - Hoboken, NJ : Wiley-Blackwell 30 (2006) 4, 725 OLC Linguistik Show details 7 Learning to use spike timing in a resticted Boltzmann machine Hinton, Geoffrey E.; Brown, Andrew D. In: Probabilistic models of the brain (Cambridge, 2002), p. 285-296 MPI für Psycholinguistik Show details 8 Simulating brain damage Hinton, Geoffrey E.; Plaut, David C.; Shallice, Tim In: Exploring cognition (London, 2000), p.409-421 MPI für Psycholinguistik Show details 9 Learning mixture models of spatial coherence Becker, Suzanna; Hinton, Geoffrey E. In: Unsupervised learning (Cambridge, MA [etc.], 1999), p. 223-234 MPI für Psycholinguistik Show details 10 The Helmholtz machine Dayan, Peter; Hinton, Geoffrey E.; Neal, Radford M.... In: Unsupervised learning (Cambridge, MA [etc.], 1999), p. 277-292 MPI für Psycholinguistik Show details 11 Learning population codes by minimizing description length Zemel, Richard S.; Hinton, Geoffrey, E. In: Unsupervised learning (Cambridge, MA [etc.], 1999), p. 261-276 MPI für Psycholinguistik Show details 12 Unsupervised learning : foundations of neural computation Hinton, Geoffrey E.; Sejnowski, T.J.. - Cambridge, MA [etc.] : MIT Press, 1999 MPI für Psycholinguistik Show details 13 Phoneme recognition using time-delay neural networks Waibel, Alexander; Hanazawa, Toshiyuki; Hinton, Geoffrey E.... In: Back-propagation (Hove, 1995), p. 35-62 MPI für Psycholinguistik Show details 14 Spatial coherence as an internal teacher for a neural network Becker, Suzanna; Hinton, Geoffrey E. In: Back-propagation (Hove, 1995), p. 313-350 MPI für Psycholinguistik 15 Learning internal representations by error propagation Rumelhart, David E.; Hinton, Geoffrey E.; Williams, R. J. In: Cognitive science ; 1. - Aldershot : Elgar (1995), 531-576 BLLDB Show details 16 Lesioning an attractor network : investigations of acquired dyslexia Hinton, Geoffrey E.; Shallice, Tim In: Psychological review. - Washington, DC [u.a.] : American Psychological Association 98 (1991) 1, 74-95 BLLDB Show details 17 Connectionist approaches McDermott, Erik (Mitarb.); Iwamida, Hitoshi (Mitarb.); Katagiri, Shigeru (Mitarb.)... In: Readings in speech recognition. - San Mateo, Calif. : Kaufmann (1990), 371-446 BLLDB Show details 18 Connectionist symbol processing Hinton, Geoffrey E. (Mitarb.); Touretzky, David S. (Mitarb.); Pollack, Jordan B. (Mitarb.)... In: Artificial intelligence. - Amsterdam : Elsevier 46 (1990) 1-2, 1-257 BLLDB Show details 19 Learning distributed representations of concepts Hinton, Geoffrey E. In: Parallel distributed processing (Oxford, 1989), P. 46-61 MPI für Psycholinguistik Show details 20 Scene-based and viewer-centered representations for comparing shapes Hinton, Geoffrey E.; Parsons, Lawrence M. In: Cognition. - Amsterdam [u.a] : Elsevier 30 (1988) 1, 1-35 BLLDB Show details Page: 1 2

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