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Neil C. Rabinowitz
Researcher at Google
Publications - 43
Citations - 10546
Neil C. Rabinowitz is an academic researcher from Google. The author has contributed to research in topics: Reinforcement learning & Artificial neural network. The author has an hindex of 23, co-authored 41 publications receiving 7150 citations. Previous affiliations of Neil C. Rabinowitz include Howard Hughes Medical Institute & University of Oxford.
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick,Razvan Pascanu,Neil C. Rabinowitz,Joel Veness,Guillaume Desjardins,Andrei Rusu,Kieran Milan,John Quan,Tiago Ramalho,Agnieszka Grabska-Barwinska,Demis Hassabis,Claudia Clopath,Dharshan Kumaran,Raia Hadsell +13 more
TL;DR: It is shown that it is possible to overcome the limitation of connectionist models and train networks that can maintain expertise on tasks that they have not experienced for a long time and selectively slowing down learning on the weights important for previous tasks.
Journal ArticleDOI
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick,Razvan Pascanu,Neil C. Rabinowitz,Joel Veness,Guillaume Desjardins,Andrei Rusu,Kieran Milan,John Quan,Tiago Ramalho,Agnieszka Grabska-Barwinska,Demis Hassabis,Claudia Clopath,Dharshan Kumaran,Raia Hadsell +13 more
TL;DR: In this paper, the authors show that it is possible to train networks that can maintain expertise on tasks that they have not experienced for a long time by selectively slowing down learning on the weights important for those tasks.
Patent
Progressive neural networks
Neil C. Rabinowitz,Guillaume Desjardins,Andrei-Alexandru Rusu,Koray Kavukcuoglu,Raia Hadsell,Razvan Pascanu,James Kirkpatrick,Hubert Josef Soyer +7 more
TL;DR: In this paper, a sequence of deep neural networks (DNNs) corresponding to a first machine learning task is presented, where the first DNN comprises a first plurality of indexed layers, and each layer in the first plurality is configured to receive a respective layer input and process the layer input to generate a corresponding layer output.
Journal ArticleDOI
Neural scene representation and rendering
S. M. Ali Eslami,Danilo Jimenez Rezende,Frederic Besse,Fabio Viola,Ari S. Morcos,Marta Garnelo,Avraham Ruderman,Andrei Rusu,Ivo Danihelka,Karol Gregor,David P. Reichert,Lars Buesing,Theophane Weber,Oriol Vinyals,Dan Rosenbaum,Neil C. Rabinowitz,Helen Dean King,Chloe Hillier,Matthew Botvinick,Daan Wierstra,Koray Kavukcuoglu,Demis Hassabis +21 more
TL;DR: The Generative Query Network (GQN) is introduced, a framework within which machines learn to represent scenes using only their own sensors, demonstrating representation learning without human labels or domain knowledge.
Journal ArticleDOI
Vector-based navigation using grid-like representations in artificial agents
Andrea Banino,Caswell Barry,Benigno Uria,Charles Blundell,Timothy P. Lillicrap,Piotr Mirowski,Alexander Pritzel,Martin J. Chadwick,Thomas Degris,Joseph Modayil,Greg Wayne,Hubert Soyer,Fabio Viola,Brian Hu Zhang,Ross Goroshin,Neil C. Rabinowitz,Razvan Pascanu,Charles Beattie,Stig Petersen,Amir Sadik,Stephen Gaffney,Helen King,Koray Kavukcuoglu,Demis Hassabis,Raia Hadsell,Dharshan Kumaran +25 more
TL;DR: These findings show that emergent grid-like representations furnish agents with a Euclidean spatial metric and associated vector operations, providing a foundation for proficient navigation, and support neuroscientific theories that see grid cells as critical for vector-based navigation.