NIPS Proceedingsβ

Geoffrey E. Hinton

58 Papers

  • Dynamic Routing Between Capsules (2017)
  • Attend, Infer, Repeat: Fast Scene Understanding with Generative Models (2016)
  • Using Fast Weights to Attend to the Recent Past (2016)
  • A Better Way to Pretrain Deep Boltzmann Machines (2012)
  • ImageNet Classification with Deep Convolutional Neural Networks (2012)
  • Gated Softmax Classification (2010)
  • Generating more realistic images using gated MRF's (2010)
  • Learning to combine foveal glimpses with a third-order Boltzmann machine (2010)
  • Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine (2010)
  • 3D Object Recognition with Deep Belief Nets (2009)
  • Replicated Softmax: an Undirected Topic Model (2009)
  • Zero-shot Learning with Semantic Output Codes (2009)
  • A Scalable Hierarchical Distributed Language Model (2008)
  • Generative versus discriminative training of RBMs for classification of fMRI images (2008)
  • Implicit Mixtures of Restricted Boltzmann Machines (2008)
  • The Recurrent Temporal Restricted Boltzmann Machine (2008)
  • Using matrices to model symbolic relationship (2008)
  • Modeling image patches with a directed hierarchy of Markov random fields (2007)
  • Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes (2007)
  • Modeling Human Motion Using Binary Latent Variables (2006)
  • Inferring Motor Programs from Images of Handwritten Digits (2005)
  • Exponential Family Harmoniums with an Application to Information Retrieval (2004)
  • Multiple Relational Embedding (2004)
  • Neighbourhood Components Analysis (2004)
  • Wormholes Improve Contrastive Divergence (2003)
  • Learning Sparse Topographic Representations with Products of Student-t Distributions (2002)
  • Self Supervised Boosting (2002)
  • Stochastic Neighbor Embedding (2002)
  • Global Coordination of Local Linear Models (2001)
  • Learning Hierarchical Structures with Linear Relational Embedding (2001)
  • Relative Density Nets: A New Way to Combine Backpropagation with HMM's (2001)
  • Rate-coded Restricted Boltzmann Machines for Face Recognition (2000)
  • Recognizing Hand-written Digits Using Hierarchical Products of Experts (2000)
  • Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task (2000)
  • Learning to Parse Images (1999)
  • Spiking Boltzmann Machines (1999)
  • Fast Neural Network Emulation of Dynamical Systems for Computer Animation (1998)
  • SMEM Algorithm for Mixture Models (1998)
  • Hierarchical Non-linear Factor Analysis and Topographic Maps (1997)
  • Does the Wake-sleep Algorithm Produce Good Density Estimators? (1995)
  • Using Pairs of Data-Points to Define Splits for Decision Trees (1995)
  • An Alternative Model for Mixtures of Experts (1994)
  • Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks (1994)
  • Recognizing Handwritten Digits Using Mixtures of Linear Models (1994)
  • Using a neural net to instantiate a deformable model (1994)
  • Autoencoders, Minimum Description Length and Helmholtz Free Energy (1993)
  • Developing Population Codes by Minimizing Description Length (1993)
  • Feudal Reinforcement Learning (1992)
  • Adaptive Elastic Models for Hand-Printed Character Recognition (1991)
  • Adaptive Soft Weight Tying using Gaussian Mixtures (1991)
  • Learning to Make Coherent Predictions in Domains with Discontinuities (1991)
  • Discovering Viewpoint-Invariant Relationships That Characterize Objects (1990)
  • Evaluation of Adaptive Mixtures of Competing Experts (1990)
  • Dimensionality Reduction and Prior Knowledge in E-Set Recognition (1989)
  • Discovering High Order Features with Mean Field Modules (1989)
  • TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations (1989)
  • GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection (1988)
  • Learning Representations by Recirculation (1987)