14.1k citations · 22.1k across the 10 of their papers we have counts for
10 papers
Cerberus: A Multi-headed Derenderer
Boyang Deng, Simon Kornblith, Geoffrey Hinton
To generalize to novel visual scenes with new viewpoints and new object poses, a visual system needs representations of the shapes of the parts of an object that are invariant to c…
Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee +1
Recent work has sought to understand the behavior of neural networks by comparing representations between layers and between different trained models. We examine methods for compar…
Distilling a Neural Network Into a Soft Decision Tree
Nicholas Frosst, Geoffrey Hinton
Deep neural networks have proved to be a very effective way to perform classification tasks. They excel when the input data is high dimensional, the relationship between the input…
Dynamic Routing Between Capsules
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton
A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of…
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton
Learning long term dependencies in recurrent networks is difficult due to vanishing and exploding gradients. To overcome this difficulty, researchers have developed sophisticated o…
Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean
A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfo…