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[1] David J. Fleet,et al. Dynamical binary latent variable models for 3D human pose tracking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[2] Geoffrey E. Hinton,et al. Learning Distributed Representations of Relational Data using Linear Relational Embedding , 2001, WIRN.
[3] Geoffrey E. Hinton,et al. Spiking Boltzmann Machines , 1999, NIPS.
[4] Geoffrey E. Hinton,et al. Modeling image patches with a directed hierarchy of Markov random fields , 2007, NIPS.
[5] Geoffrey E. Hinton,et al. Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.
[6] Geoffrey E. Hinton,et al. Matrix capsules with EM routing , 2018, ICLR.
[7] Geoffrey E. Hinton,et al. Lookahead Optimizer: k steps forward, 1 step back , 2019, NeurIPS.
[8] Andrea Tagliasacchi,et al. NASA: Neural Articulated Shape Approximation , 2020, ECCV.
[9] Geoffrey E. Hinton,et al. Multiple Relational Embedding , 2004, NIPS.
[10] Geoffrey E. Hinton,et al. Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine , 2009, ESANN.
[11] 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.
[12] Yee Whye Teh,et al. Discovering Multiple Constraints that are Frequently Approximately Satisfied , 2001, UAI.
[13] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[14] Geoffrey E. Hinton. Connectionist Learning Procedures , 1989, Artif. Intell..
[15] Geoffrey E. Hinton,et al. Probabilistic sequential independent components analysis , 2004, IEEE Transactions on Neural Networks.
[16] Yee Whye Teh,et al. Rate-coded Restricted Boltzmann Machines for Face Recognition , 2000, NIPS.
[17] Geoffrey E. Hinton,et al. Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..
[18] Geoffrey E. Hinton. Shape Representation in Parallel Systems , 1981, IJCAI.
[19] Geoffrey E. Hinton,et al. Deep Mixtures of Factor Analysers , 2012, ICML.
[20] Geoffrey E. Hinton,et al. Discovering Viewpoint-Invariant Relationships That Characterize Objects , 1990, NIPS.
[21] Geoffrey E. Hinton,et al. Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls , 1997, IEEE Trans. Neural Networks.
[22] Geoffrey E. Hinton,et al. Learning Sparse Topographic Representations with Products of Student-t Distributions , 2002, NIPS.
[23] Brian Sallans,et al. A Hierarchical Community of Experts , 1999, Learning in Graphical Models.
[24] Geoffrey E. Hinton,et al. Using Fast Weights to Attend to the Recent Past , 2016, NIPS.
[25] Geoffrey E. Hinton,et al. Glove-Talk: a neural network interface between a data-glove and a speech synthesizer , 1993, IEEE Trans. Neural Networks.
[26] Geoffrey E. Hinton,et al. Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates , 2000, J. VLSI Signal Process..
[27] Geoffrey E. Hinton,et al. SMEM Algorithm for Mixture Models , 1998, Neural Computation.
[28] Geoffrey E. Hinton,et al. On deep generative models with applications to recognition , 2011, CVPR 2011.
[29] Geoffrey E. Hinton,et al. An Efficient Learning Procedure for Deep Boltzmann Machines , 2012, Neural Computation.
[30] Geoffrey E. Hinton,et al. Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines , 1983, AAAI.
[31] Alex Waibel,et al. Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.
[32] Geoffrey E. Hinton,et al. Distributed Representations , 1986, The Philosophy of Artificial Intelligence.
[33] Geoffrey E. Hinton,et al. Implicit Mixtures of Restricted Boltzmann Machines , 2008, NIPS.
[34] Geoffrey E. Hinton,et al. Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.
[35] Nitish Srivastava,et al. Modeling Documents with Deep Boltzmann Machines , 2013, UAI.
[36] Geoffrey E. Hinton,et al. Comparing Classification Methods for Longitudinal fMRI Studies , 2010, Neural Computation.
[37] Geoffrey E. Hinton,et al. Adaptive Soft Weight Tying using Gaussian Mixtures , 1991, NIPS.
[38] Geoffrey E. Hinton,et al. 3D Object Recognition with Deep Belief Nets , 2009, NIPS.
[39] Geoffrey E. Hinton,et al. Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space , 2000, ICML.
[40] Geoffrey E. Hinton,et al. An Alternative Model for Mixtures of Experts , 1994, NIPS.
[41] Geoffrey E. Hinton,et al. The Recurrent Temporal Restricted Boltzmann Machine , 2008, NIPS.
[42] Geoffrey E. Hinton,et al. Improving a statistical language model by modulating the effects of context words , 2008, ESANN.
[43] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[44] Geoffrey E. Hinton,et al. Symbols Among the Neurons: Details of a Connectionist Inference Architecture , 1985, IJCAI.
[45] Geoffrey E. Hinton,et al. Temporal-Kernel Recurrent Neural Networks , 2010, Neural Networks.
[46] Geoffrey E. Hinton,et al. Instantiating Deformable Models with a Neural Net , 1997, Comput. Vis. Image Underst..
[47] Geoffrey E. Hinton,et al. A New Learning Algorithm for Mean Field Boltzmann Machines , 2002, ICANN.
[48] Geoffrey E. Hinton,et al. Products of Hidden Markov Models: It Takes N>1 to Tango , 2009, UAI.
[49] Geoffrey E. Hinton,et al. A Simple Way to Initialize Recurrent Networks of Rectified Linear Units , 2015, ArXiv.
[50] Geoffrey E. Hinton,et al. A Better Way to Pretrain Deep Boltzmann Machines , 2012, NIPS.
[51] Geoffrey E. Hinton,et al. Topographic Product Models Applied to Natural Scene Statistics , 2006, Neural Computation.
[52] Geoffrey E. Hinton,et al. Canonical Capsules: Unsupervised Capsules in Canonical Pose , 2020, ArXiv.
[53] Geoffrey E. Hinton,et al. Factored conditional restricted Boltzmann Machines for modeling motion style , 2009, ICML '09.
[54] Geoffrey E. Hinton,et al. Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.
[55] Geoffrey E. Hinton,et al. A Mobile Robot That Learns Its Place , 1997, Neural Computation.
[56] Geoffrey E. Hinton,et al. Gated Softmax Classification , 2010, NIPS.
[57] Geoffrey E. Hinton,et al. Learning Population Codes by Minimizing Description Length , 1993, Neural Computation.
[58] Geoffrey E. Hinton,et al. Wormholes Improve Contrastive Divergence , 2003, NIPS.
[59] Geoffrey E. Hinton,et al. Application of Deep Belief Networks for Natural Language Understanding , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[60] Geoffrey E. Hinton. A Parallel Computation that Assigns Canonical Object-Based Frames of Reference , 1981, IJCAI.
[61] Geoffrey E. Hinton,et al. Replicated Softmax: an Undirected Topic Model , 2009, NIPS.
[62] Geoffrey E. Hinton,et al. Dimensionality Reduction and Prior Knowledge in E-Set Recognition , 1989, NIPS.
[63] Geoffrey E. Hinton,et al. Tensor Analyzers , 2013, ICML.
[64] Geoffrey E. Hinton,et al. Improving dimensionality reduction with spectral gradient descent , 2005, Neural Networks.
[65] Geoffrey E. Hinton,et al. Modeling Natural Images Using Gated MRFs , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[66] Geoffrey E. Hinton,et al. Dynamic Routing Between Capsules , 2017, NIPS.
[67] Brian Kingsbury,et al. New types of deep neural network learning for speech recognition and related applications: an overview , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[68] Geoffrey E. Hinton,et al. Glove-TalkII: an adaptive gesture-to-formant interface , 1995, CHI '95.
[69] Geoffrey E. Hinton,et al. Generating more realistic images using gated MRF's , 2010, NIPS.
[70] Geoffrey E. Hinton,et al. Grammar as a Foreign Language , 2014, NIPS.
[71] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[72] Andrea Tagliasacchi,et al. CvxNet: Learnable Convex Decomposition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[73] Geoffrey E. Hinton. A better way to learn features: technical perspective , 2011, CACM.
[74] Geoffrey E. Hinton,et al. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer , 2017, ICLR.
[75] Yee Whye Teh,et al. Learning to Parse Images , 1999, NIPS.
[76] Geoffrey E. Hinton,et al. Inferring Motor Programs from Images of Handwritten Digits , 2005, NIPS.
[77] Geoffrey E. Hinton,et al. Three new graphical models for statistical language modelling , 2007, ICML '07.
[78] Geoffrey E. Hinton,et al. Deep Lambertian Networks , 2012, ICML.
[79] Geoffrey E. Hinton,et al. Developing Population Codes by Minimizing Description Length , 1993, NIPS.
[80] Yee Whye Teh,et al. Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation , 2006, Cogn. Sci..
[81] Geoffrey E. Hinton,et al. Two Distributed-State Models For Generating High-Dimensional Time Series , 2011, J. Mach. Learn. Res..
[82] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[83] Geoffrey E. Hinton,et al. Connectionist Architectures for Artificial Intelligence , 1990, Computer.
[84] Geoffrey E. Hinton,et al. Learning Hierarchical Structures with Linear Relational Embedding , 2001, NIPS.
[85] Geoffrey E. Hinton,et al. Deep, Narrow Sigmoid Belief Networks Are Universal Approximators , 2008, Neural Computation.
[86] Geoffrey E. Hinton,et al. Unsupervised Learning of Image Transformations , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[87] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[88] Geoffrey E. Hinton. Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space , 1989, Neural Computation.
[89] Geoffrey E. Hinton,et al. A soft decision-directed LMS algorithm for blind equalization , 1993, IEEE Trans. Commun..
[90] Geoffrey E. Hinton,et al. Extracting distributed representations of concepts and relations from positive and negative propositions , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[91] Geoffrey E. Hinton,et al. Using Generative Models for Handwritten Digit Recognition , 1996, IEEE Trans. Pattern Anal. Mach. Intell..
[92] Geoffrey E. Hinton,et al. Learning Causally Linked Markov Random Fields , 2005, AISTATS.
[93] Geoffrey E. Hinton,et al. Guest Editorial: Deep Learning , 2015, International Journal of Computer Vision.
[94] Takeo Kanade,et al. Representation and Control in Vision , 1983 .
[95] Geoffrey E. Hinton,et al. Using Pairs of Data-Points to Define Splits for Decision Trees , 1995, NIPS.
[96] Geoffrey E. Hinton,et al. Using very deep autoencoders for content-based image retrieval , 2011, ESANN.
[97] Yee Whye Teh,et al. Energy-Based Models for Sparse Overcomplete Representations , 2003, J. Mach. Learn. Res..
[98] Geoffrey E. Hinton,et al. Self Supervised Boosting , 2002, NIPS.
[99] Geoffrey Hinton. The Next Generation of Neural Networks , 2020, SIGIR.
[100] Brendan J. Frey,et al. Efficient Stochastic Source Coding and an Application to a Bayesian Network Source Model , 1997, Comput. J..
[101] Geoffrey E. Hinton,et al. Learning Distributed Representations of Concepts Using Linear Relational Embedding , 2001, IEEE Trans. Knowl. Data Eng..
[102] Geoffrey E. Hinton,et al. Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.
[103] Geoffrey E. Hinton,et al. Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search , 2018, ACL.
[104] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[105] Geoffrey E. Hinton,et al. Visualizing non-metric similarities in multiple maps , 2011, Machine Learning.
[106] Geoffrey E. Hinton,et al. Neural Additive Models: Interpretable Machine Learning with Neural Nets , 2020, NeurIPS.
[107] Geoffrey E. Hinton,et al. Robust Boltzmann Machines for recognition and denoising , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[108] Geoffrey E. Hinton,et al. Discovering Binary Codes for Documents by Learning Deep Generative Models , 2011, Top. Cogn. Sci..
[109] P. Willeit,et al. Sauna bathing reduces the risk of stroke in Finnish men and women , 2018, Neurology.
[110] Geoffrey E. Hinton,et al. Analysis-by-Synthesis by Learning to Invert Generative Black Boxes , 2008, ICANN.
[111] Geoffrey E. Hinton. Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery , 1979, Cogn. Sci..
[112] Geoffrey E. Hinton,et al. Learning Representations by Recirculation , 1987, NIPS.
[113] Geoffrey E. Hinton,et al. Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks , 1994, NIPS.
[114] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[115] Geoffrey E. Hinton,et al. Coaching variables for regression and classification , 1998, Stat. Comput..
[116] Geoffrey E. Hinton,et al. Using matrices to model symbolic relationship , 2008, NIPS.
[117] Geoffrey E. Hinton,et al. Stochastic Neighbor Embedding , 2002, NIPS.
[118] Geoffrey E. Hinton,et al. A Desktop Input Device and Interface for Interactive 3D Character Animation , 2002, Graphics Interface.
[119] Brendan J. Frey,et al. Free energy coding , 1996, Proceedings of Data Compression Conference - DCC '96.
[120] Geoffrey E. Hinton,et al. NeuroAnimator: fast neural network emulation and control of physics-based models , 1998, SIGGRAPH.
[121] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[122] Geoffrey E. Hinton,et al. Modeling the manifolds of images of handwritten digits , 1997, IEEE Trans. Neural Networks.
[123] Brendan J. Frey,et al. Does the Wake-sleep Algorithm Produce Good Density Estimators? , 1995, NIPS.
[124] Geoffrey E. Hinton. Modeling High-Dimensional Data by Combining Simple Experts , 2000, AAAI/IAAI.
[125] Geoffrey E. Hinton,et al. Recognizing Handwritten Digits Using Mixtures of Linear Models , 1994, NIPS.
[126] Geoffrey E. Hinton,et al. Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls , 1997, IEEE Trans. Neural Networks.
[127] Geoffrey E. Hinton. Mapping Part-Whole Hierarchies into Connectionist Networks , 1990, Artif. Intell..
[128] Geoffrey E. Hinton,et al. The Bootstrap Widrow-Hoff Rule as a Cluster-Formation Algorithm , 1990, Neural Computation.
[129] Yee Whye Teh,et al. Stacked Capsule Autoencoders , 2019, NeurIPS.
[130] Geoffrey E. Hinton,et al. Building adaptive interfaces with neural networks: The glove-talk pilot study , 1990, INTERACT.
[131] Geoffrey E. Hinton,et al. Transforming Auto-Encoders , 2011, ICANN.
[132] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[133] Frits C. R. Spieksma,et al. Boltzmann Machines , 1995, Artificial Neural Networks.
[134] Geoffrey E. Hinton,et al. Learning Generative Texture Models with extended Fields-of-Experts , 2009, BMVC.
[135] Geoffrey E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.
[136] Geoffrey E. Hinton,et al. Autoregressive product of multi-frame predictions can improve the accuracy of hybrid models , 2014, INTERSPEECH.
[137] Brendan J. Frey,et al. Variational Learning in Nonlinear Gaussian Belief Networks , 1999, Neural Computation.
[138] Geoffrey E. Hinton. Using Relaxation to find a Puppet , 1976, AISB.
[139] Geoffrey E. Hinton,et al. A time-delay neural network architecture for isolated word recognition , 1990, Neural Networks.
[140] Geoffrey E. Hinton,et al. Generative versus discriminative training of RBMs for classification of fMRI images , 2008, NIPS.
[141] Geoffrey E. Hinton,et al. GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection , 1988, NIPS.
[142] Geoffrey E. Hinton,et al. Global Coordination of Local Linear Models , 2001, NIPS.
[143] Geoffrey E. Hinton,et al. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models , 2016, NIPS.
[144] Geoffrey E. Hinton,et al. Evaluation of Adaptive Mixtures of Competing Experts , 1990, NIPS.
[145] Geoffrey E. Hinton,et al. Learning Multilevel Distributed Representations for High-Dimensional Sequences , 2007, AISTATS.
[146] Geoffrey E. Hinton,et al. Generating Text with Recurrent Neural Networks , 2011, ICML.
[147] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[148] Geoffrey E. Hinton,et al. Efficient Parametric Projection Pursuit Density Estimation , 2003, UAI.
[149] Geoffrey E. Hinton. Learning Translation Invariant Recognition in Massively Parallel Networks , 1987, PARLE.
[150] Geoffrey E. Hinton,et al. To appear in : Advances in Neural Information Processing Systems , 2007 .
[151] Geoffrey E. Hinton,et al. Semantic hashing , 2009, Int. J. Approx. Reason..
[152] Geoffrey E. Hinton,et al. Local Physical Models for Interactive Character Animation , 2002, Comput. Graph. Forum.
[153] Geoffrey E. Hinton,et al. Zero-shot Learning with Semantic Output Codes , 2009, NIPS.
[154] Geoffrey E. Hinton,et al. Shape Recognition and Illusory Conjunctions , 1985, IJCAI.
[155] Geoffrey E. Hinton,et al. Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines , 2010, Neural Computation.
[156] Geoffrey E. Hinton,et al. Binary coding of speech spectrograms using a deep auto-encoder , 2010, INTERSPEECH.
[157] Geoffrey E. Hinton,et al. Adaptive Elastic Models for Hand-Printed Character Recognition , 1991, NIPS.
[158] Geoffrey E. Hinton,et al. Reinforcement Learning with Factored States and Actions , 2004, J. Mach. Learn. Res..
[159] David J. Fleet,et al. Unsupervised part representation by Flow Capsules , 2020, ICML.
[160] Geoffrey E. Hinton,et al. Using fast weights to improve persistent contrastive divergence , 2009, ICML '09.
[161] Geoffrey E. Hinton,et al. Who Said What: Modeling Individual Labelers Improves Classification , 2017, AAAI.
[162] Geoffrey E. Hinton. Relaxation and its role in vision , 1977 .
[163] Geoffrey E. Hinton,et al. Distilling a Neural Network Into a Soft Decision Tree , 2017, CEx@AI*IA.
[164] Geoffrey E. Hinton,et al. A Distributed Connectionist Production System , 1988, Cogn. Sci..
[165] Geoffrey E. Hinton,et al. Learning a better representation of speech soundwaves using restricted boltzmann machines , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[166] Geoffrey E. Hinton,et al. Fast Neural Network Emulation of Dynamical Systems for Computer Animation , 1998, NIPS.
[167] Geoffrey E. Hinton,et al. Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.
[168] Geoffrey E. Hinton,et al. Workshop summary: Workshop on learning feature hierarchies , 2009, ICML '09.
[169] Geoffrey E. Hinton,et al. Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images , 2010, AISTATS.
[170] Geoffrey E. Hinton,et al. Large scale distributed neural network training through online distillation , 2018, ICLR.
[171] Geoffrey E. Hinton. What kind of graphical model is the brain? , 2005, IJCAI.
[172] Geoffrey E. Hinton,et al. Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions , 2019, ICLR.
[173] Geoffrey E. Hinton,et al. Learning to combine foveal glimpses with a third-order Boltzmann machine , 2010, NIPS.
[174] Geoffrey E. Hinton,et al. Using an autoencoder with deformable templates to discover features for automated speech recognition , 2013, INTERSPEECH.
[175] Geoffrey E. Hinton,et al. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure , 2007, AISTATS.
[176] Drew McDermott,et al. Learning in Massively Parallel Nets (Panel) , 1986, AAAI.
[177] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[178] Geoffrey E. Hinton,et al. Learning fast neural network emulators for physics-based models , 1997, International Conference on Computer Graphics and Interactive Techniques.
[179] Geoffrey E. Hinton,et al. Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes , 2007, NIPS.
[180] Geoffrey E. Hinton,et al. Simplifying Neural Networks by Soft Weight-Sharing , 1992, Neural Computation.
[181] Geoffrey E. Hinton. Deep Belief Nets , 2017, Encyclopedia of Machine Learning and Data Mining.
[182] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[183] Geoffrey E. Hinton,et al. Recognizing Hand-written Digits Using Hierarchical Products of Experts , 2002, NIPS.
[184] Geoffrey E. Hinton,et al. Modeling the joint density of two images under a variety of transformations , 2011, CVPR 2011.
[185] Geoffrey E. Hinton,et al. Similarity of Neural Network Representations Revisited , 2019, ICML.
[186] Geoffrey E. Hinton,et al. Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.
[187] Jen-Tzung Chien,et al. Introduction to the Special Section on Deep Learning for Speech and Language Processing , 2012, IEEE Transactions on Audio, Speech, and Language Processing.
[188] Geoffrey E. Hinton. Where Do Features Come From? , 2014, Cogn. Sci..
[189] Tafsir Thiam,et al. The Boltzmann machine , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).
[190] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[191] Geoffrey E. Hinton,et al. On rectified linear units for speech processing , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[192] Geoffrey E. Hinton,et al. Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures , 2018, NeurIPS.
[193] Bhuvana Ramabhadran,et al. Deep belief nets for natural language call-routing , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[194] Geoffrey E. Hinton,et al. Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task , 2000, NIPS.
[195] Geoffrey E. Hinton,et al. How Learning Can Guide Evolution , 1996, Complex Syst..
[196] Geoffrey E. Hinton,et al. A Scalable Hierarchical Distributed Language Model , 2008, NIPS.
[197] Geoffrey E. Hinton,et al. TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations , 1989, NIPS.