Deep Learning of Representations
暂无分享,去创建一个
[1] D. Hubel,et al. Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.
[2] Heikki Riittinen,et al. Spectral classification of phonemes by learning subspaces , 1979, ICASSP.
[3] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[4] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[5] Johan Håstad,et al. Almost optimal lower bounds for small depth circuits , 1986, STOC '86.
[6] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[7] Teuvo Kohonen,et al. The self-organizing map , 1990 .
[8] Suzanna Becker,et al. Learning to Categorize Objects Using Temporal Coherence , 1992, NIPS.
[9] A. Grinvald,et al. Relationships between orientation-preference pinwheels, cytochrome oxidase blobs, and ocular-dominance columns in primate striate cortex. , 1992, Proceedings of the National Academy of Sciences of the United States of America.
[10] Andrew R. Barron,et al. Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.
[11] Geoffrey E. Hinton,et al. Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.
[12] D. V. van Essen,et al. A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[13] Yann LeCun,et al. Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..
[14] Geoffrey E. Hinton,et al. Learning Mixture Models of Spatial Coherence , 1993, Neural Computation.
[15] I. Guyon,et al. Advances in pattern recognition systems using neural network technologies , 1994 .
[16] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[17] Teuvo Kohonen,et al. Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map , 1996, Biological Cybernetics.
[18] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[19] Yoshua Bengio,et al. Global training of document processing systems using graph transformer networks , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[20] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[21] Michael I. Jordan. Learning in Graphical Models , 1999, NATO ASI Series.
[22] Jean-François Cardoso,et al. Multidimensional independent component analysis , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[23] Geoffrey E. Hinton. Products of experts , 1999 .
[24] L. Younes. On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates , 1999 .
[25] Yoshua Bengio,et al. A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..
[26] Joshua B. Tenenbaum,et al. Separating Style and Content with Bilinear Models , 2000, Neural Computation.
[27] Aapo Hyvärinen,et al. Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.
[28] Alexander J. Smola,et al. Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.
[29] Aapo Hyvärinen,et al. Topographic Independent Component Analysis , 2001, Neural Computation.
[30] Aapo Hyvärinen,et al. Temporal Coherence, Natural Image Sequences, and the Visual Cortex , 2002, NIPS.
[31] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[32] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[33] Lawrence K. Saul,et al. Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..
[34] Konrad Paul Kording,et al. How are complex cell properties adapted to the statistics of natural stimuli? , 2004, Journal of neurophysiology.
[35] Geoffrey E. Hinton,et al. Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.
[36] John Blitzer,et al. Hierarchical Distributed Representations for Statistical Language Modeling , 2004, NIPS.
[37] Trevor Darrell,et al. Conditional Random Fields for Object Recognition , 2004, NIPS.
[38] Alan L. Yuille,et al. The Convergence of Contrastive Divergences , 2004, NIPS.
[39] H. Bourlard,et al. Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.
[40] Demetri Terzopoulos,et al. Multilinear independent components analysis , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[41] Rajesh P. N. Rao,et al. Bilinear Sparse Coding for Invariant Vision , 2005, Neural Computation.
[42] David J. Field,et al. How Close Are We to Understanding V1? , 2005, Neural Computation.
[43] Laurenz Wiskott,et al. Slow feature analysis yields a rich repertoire of complex cell properties. , 2005, Journal of vision.
[44] Johan Håstad,et al. On the power of small-depth threshold circuits , 1991, computational complexity.
[45] Aapo Hyvärinen,et al. Estimation of Non-Normalized Statistical Models by Score Matching , 2005, J. Mach. Learn. Res..
[46] Nicolas Le Roux,et al. The Curse of Highly Variable Functions for Local Kernel Machines , 2005, NIPS.
[47] Yoshua Bengio,et al. Hierarchical Probabilistic Neural Network Language Model , 2005, AISTATS.
[48] Philipp Slusallek,et al. Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.
[49] Yann LeCun,et al. Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[50] Masoud Nikravesh,et al. Feature Extraction - Foundations and Applications , 2006, Feature Extraction.
[51] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[52] Nicolas Le Roux,et al. Spectral Dimensionality Reduction , 2006, Feature Extraction.
[53] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[54] Yann LeCun,et al. Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[55] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[56] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[57] Michael S. Lewicki,et al. Efficient auditory coding , 2006, Nature.
[58] Rajat Raina,et al. Efficient sparse coding algorithms , 2006, NIPS.
[59] Fernando Pereira,et al. Structured Learning with Approximate Inference , 2007, NIPS.
[60] Jason Weston,et al. Large-scale kernel machines , 2007 .
[61] Yoshua Bengio,et al. Scaling learning algorithms towards AI , 2007 .
[62] Thomas Serre,et al. A quantitative theory of immediate visual recognition. , 2007, Progress in brain research.
[63] Rajat Raina,et al. Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.
[64] Michel Verleysen,et al. Nonlinear Dimensionality Reduction , 2021, Computer Vision.
[65] Geoffrey E. Hinton,et al. Three new graphical models for statistical language modelling , 2007, ICML '07.
[66] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[67] Nicolas Le Roux,et al. Representational Power of Restricted Boltzmann Machines and Deep Belief Networks , 2008, Neural Computation.
[68] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[69] Thierry Bertin-Mahieux,et al. On the Use of Sparce Time Relative Auditory Codes for Music , 2008, ISMIR.
[70] Bruno A. Olshausen,et al. Learning Transformational Invariants from Natural Movies , 2008, NIPS.
[71] Tijmen Tieleman,et al. Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.
[72] Jason Weston,et al. Deep learning via semi-supervised embedding , 2008, ICML '08.
[73] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[74] Marcello Sanguineti,et al. Geometric Upper Bounds on Rates of Variable-Basis Approximation , 2008, IEEE Transactions on Information Theory.
[75] Yoshua Bengio,et al. Classification using discriminative restricted Boltzmann machines , 2008, ICML '08.
[76] Alex Graves,et al. Supervised Sequence Labelling with Recurrent Neural Networks , 2012, Studies in Computational Intelligence.
[77] Geoffrey E. Hinton,et al. A Scalable Hierarchical Distributed Language Model , 2008, NIPS.
[78] H. Sebastian Seung,et al. Natural Image Denoising with Convolutional Networks , 2008, NIPS.
[79] David M. Bradley,et al. Differentiable Sparse Coding , 2008, NIPS.
[80] Yoshua Bengio,et al. Zero-data Learning of New Tasks , 2008, AAAI.
[81] Geoffrey E. Hinton,et al. Using fast weights to improve persistent contrastive divergence , 2009, ICML '09.
[82] Yoshua Bengio,et al. Slow, Decorrelated Features for Pretraining Complex Cell-like Networks , 2009, NIPS.
[83] Frank Hutter,et al. Automated configuration of algorithms for solving hard computational problems , 2009 .
[84] Marc'Aurelio Ranzato,et al. Learning invariant features through topographic filter maps , 2009, CVPR.
[85] Honglak Lee,et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.
[86] Yann LeCun,et al. What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[87] Quoc V. Le,et al. Measuring Invariances in Deep Networks , 2009, NIPS.
[88] Max Welling,et al. Herding Dynamic Weights for Partially Observed Random Field Models , 2009, UAI.
[89] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[90] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[91] Cordelia Schmid,et al. Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.
[92] Yoshua Bengio,et al. Justifying and Generalizing Contrastive Divergence , 2009, Neural Computation.
[93] Honglak Lee,et al. Unsupervised feature learning for audio classification using convolutional deep belief networks , 2009, NIPS.
[94] Wolfgang Maass,et al. Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks , 2009, NIPS.
[95] P. Dayan,et al. Flexible shaping: How learning in small steps helps , 2009, Cognition.
[96] Hossein Mobahi,et al. Deep learning from temporal coherence in video , 2009, ICML '09.
[97] Hugo Larochelle,et al. Efficient Learning of Deep Boltzmann Machines , 2010, AISTATS.
[98] Quoc V. Le,et al. Tiled convolutional neural networks , 2010, NIPS.
[99] Yoshua Bengio,et al. Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.
[100] Samy Bengio,et al. Large Scale Online Learning of Image Similarity Through Ranking , 2009, J. Mach. Learn. Res..
[101] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[102] Yann LeCun,et al. Convolutional Learning of Spatio-temporal Features , 2010, ECCV.
[103] Ruslan Salakhutdinov,et al. Learning Deep Boltzmann Machines using Adaptive MCMC , 2010, ICML.
[104] Yann LeCun,et al. Regularized estimation of image statistics by Score Matching , 2010, NIPS.
[105] Geoffrey E. Hinton,et al. Generating more realistic images using gated MRF's , 2010, NIPS.
[106] Pascal Vincent,et al. Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines , 2010, AISTATS.
[107] Y-Lan Boureau,et al. Learning Convolutional Feature Hierarchies for Visual Recognition , 2010, NIPS.
[108] Jason Weston,et al. Label Embedding Trees for Large Multi-Class Tasks , 2010, NIPS.
[109] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[110] Jason Weston,et al. Large scale image annotation: learning to rank with joint word-image embeddings , 2010, Machine Learning.
[111] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[112] Marc'Aurelio Ranzato,et al. Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition , 2010, ArXiv.
[113] Thomas Deselaers,et al. ClassCut for Unsupervised Class Segmentation , 2010, ECCV.
[114] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[115] Geoffrey E. Hinton,et al. Modeling pixel means and covariances using factorized third-order boltzmann machines , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[116] Aapo Hyvärinen,et al. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.
[117] Yann LeCun,et al. Learning Fast Approximations of Sparse Coding , 2010, ICML.
[118] Razvan Pascanu,et al. Deep Self-Taught Learning for Handwritten Character Recognition , 2010, ArXiv.
[119] Joseph F. Murray,et al. Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation , 2010, Neural Computation.
[120] Yoshua Bengio,et al. Algorithms for Hyper-Parameter Optimization , 2011, NIPS.
[121] Veselin Stoyanov,et al. Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure , 2011, AISTATS.
[122] Quoc V. Le,et al. On optimization methods for deep learning , 2011, ICML.
[123] Lukás Burget,et al. Extensions of recurrent neural network language model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[124] Tapani Raiko,et al. Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines , 2011, ICML.
[125] Pascal Vincent,et al. Higher Order Contractive Auto-Encoder , 2011, ECML/PKDD.
[126] Razvan Pascanu,et al. Deep Learners Benefit More from Out-of-Distribution Examples , 2011, AISTATS.
[127] Yoshua Bengio,et al. Shallow vs. Deep Sum-Product Networks , 2011, NIPS.
[128] Mark Braverman. Poly-logarithmic independence fools bounded-depth boolean circuits , 2011, CACM.
[129] Jason Weston,et al. WSABIE: Scaling Up to Large Vocabulary Image Annotation , 2011, IJCAI.
[130] Yoshua Bengio,et al. Unsupervised Models of Images by Spikeand-Slab RBMs , 2011, ICML.
[131] Yoshua Bengio,et al. Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.
[132] Andrew Y. Ng,et al. The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization , 2011, ICML.
[133] Lukás Burget,et al. Empirical Evaluation and Combination of Advanced Language Modeling Techniques , 2011, INTERSPEECH.
[134] Yoshua Bengio,et al. A Spike and Slab Restricted Boltzmann Machine , 2011, AISTATS.
[135] Pascal Vincent,et al. A Connection Between Score Matching and Denoising Autoencoders , 2011, Neural Computation.
[136] Pascal Vincent,et al. The Manifold Tangent Classifier , 2011, NIPS.
[137] Yoshua Bengio,et al. On the Expressive Power of Deep Architectures , 2011, ALT.
[138] Francis R. Bach,et al. Structured Variable Selection with Sparsity-Inducing Norms , 2009, J. Mach. Learn. Res..
[139] Andrew Y. Ng,et al. Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.
[140] Yoshua Bengio,et al. Large-Scale Learning of Embeddings with Reconstruction Sampling , 2011, ICML.
[141] Jason Weston,et al. Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..
[142] Lukás Burget,et al. Strategies for training large scale neural network language models , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.
[143] Pascal Vincent,et al. Quickly Generating Representative Samples from an RBM-Derived Process , 2011, Neural Computation.
[144] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[145] Yoshua Bengio,et al. Large-Scale Feature Learning With Spike-and-Slab Sparse Coding , 2012, ICML.
[146] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[147] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[148] Marc'Aurelio Ranzato,et al. Large Scale Distributed Deep Networks , 2012, NIPS.
[149] Yoshua Bengio,et al. Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.
[150] Yoshua Bengio,et al. Unsupervised and Transfer Learning Challenge: a Deep Learning Approach , 2011, ICML Unsupervised and Transfer Learning.
[151] Yoshua Bengio,et al. Evolving Culture vs Local Minima , 2012, ArXiv.
[152] Matthew D. Zeiler. ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.
[153] Yoshua Bengio,et al. A Generative Process for sampling Contractive Auto-Encoders , 2012, ICML 2012.
[154] Geoffrey E. Hinton,et al. Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[155] Geoffrey E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.
[156] Jason Weston,et al. Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing , 2012, AISTATS.
[157] Pascal Vincent,et al. Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.
[158] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[159] Yoshua Bengio,et al. Deep Learning of Representations: Looking Forward , 2013, SLSP.
[160] Yoshua Bengio,et al. Joint Training Deep Boltzmann Machines for Classification , 2013, ICLR.
[161] Razvan Pascanu,et al. On the difficulty of training recurrent neural networks , 2012, ICML.
[162] Léon Bottou,et al. From machine learning to machine reasoning , 2011, Machine Learning.
[163] Yoshua Bengio,et al. Better Mixing via Deep Representations , 2012, ICML.
[164] Razvan Pascanu,et al. Advances in optimizing recurrent networks , 2012, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[165] Yoshua Bengio,et al. What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..
[166] Yoshua Bengio,et al. Deep Generative Stochastic Networks Trainable by Backprop , 2013, ICML.