Geoffrey E. Hinton

32 papers · 2003–2022 · 3 conferences · across top CS/AI conferences

Achievements

+15 more ↓

🧭 Keyword Pioneer 🗺️ Taxonomy Completionist (30) 🌉 Interdisciplinary Bridge 🌈 Renaissance Researcher (7) 🐣 Hot Topic Early Bird

🌍 Conference Polyglot (3) 🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (30) 🏠 Conference Loyalist (28) 🌟 Keyword Trendsetter Combo (6) 🏆 Keyword Champion 🔬 Deep Specialist (16) 🌱 Topic Pioneer 🗃️ Keyword Collector (114) ⚡ Prolific Year (5) 🚀 Conference Pioneer 📈 Trend Setter ❓ The Questioner 💎 Century Club (32) 🔥 Unstoppable (7)

Conferences

NIPS (28) JMLR (3) ICLR (1)

Top co-authors

Graham W. Taylor (3) Ilya Sutskever (3) Ruslan Salakhutdinov (3) Sara Sabour (3) Vinod Nair (2) Simon Kornblith (2) Jimmy Ba (2) Sam T. Roweis (2) Volodymyr Mnih (2) Ting Chen (2)

Keywords

generative model (11) neural network (8) restricted boltzmann machine (5) deep belief network (5) image classification (4) latent variable (3) capsule network (3) convolutional neural network (3) motion capture (3) deep belief net (3) object recognition (3) unsupervised learning (3) attention mechanism (3) markov random field (2) probabilistic model (2) feature learning (2) boltzmann machine (2) latent variable model (2) semi-supervised learning (2) knowledge distillation (2)

Papers

A Unified Sequence Interface for Vision Tasks NIPS 2022 Canonical Capsules: Self-Supervised Capsules in Canonical Pose NIPS 2021 Neural Additive Models: Interpretable Machine Learning with Neural Nets NIPS 2021 Big Self-Supervised Models are Strong Semi-Supervised Learners NIPS 2020 Lookahead Optimizer: k steps forward, 1 step back NIPS 2019 When does label smoothing help? NIPS 2019 Stacked Capsule Autoencoders NIPS 2019 Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures NIPS 2018 Large scale distributed neural network training through online distillation ICLR 2018 Dynamic Routing Between Capsules NIPS 2017 Using Fast Weights to Attend to the Recent Past NIPS 2016 Attend, Infer, Repeat: Fast Scene Understanding with Generative Models NIPS 2016 ImageNet Classification with Deep Convolutional Neural Networks NIPS 2012 A Better Way to Pretrain Deep Boltzmann Machines NIPS 2012 Two Distributed-State Models For Generating High-Dimensional Time Series JMLR 2011 Generating more realistic images using gated MRF's NIPS 2010 Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine NIPS 2010 Learning to combine foveal glimpses with a third-order Boltzmann machine NIPS 2010 Gated Softmax Classification NIPS 2010 3D Object Recognition with Deep Belief Nets NIPS 2009 Replicated Softmax: an Undirected Topic Model NIPS 2009 Zero-shot Learning with Semantic Output Codes NIPS 2009 Using matrices to model symbolic relationship NIPS 2008 Implicit Mixtures of Restricted Boltzmann Machines NIPS 2008 The Recurrent Temporal Restricted Boltzmann Machine NIPS 2008 Generative versus discriminative training of RBMs for classification of fMRI images NIPS 2008 A Scalable Hierarchical Distributed Language Model NIPS 2008 Modeling image patches with a directed hierarchy of Markov random fields NIPS 2007 Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes NIPS 2007 Modeling Human Motion Using Binary Latent Variables NIPS 2006 Reinforcement Learning with Factored States and Actions JMLR 2004 Energy-Based Models for Sparse Overcomplete Representations JMLR 2003