D
David Raposo
Researcher at Cold Spring Harbor Laboratory
Publications - 35
Citations - 6149
David Raposo is an academic researcher from Cold Spring Harbor Laboratory. The author has contributed to research in topics: Artificial neural network & Reinforcement learning. The author has an hindex of 19, co-authored 30 publications receiving 4803 citations. Previous affiliations of David Raposo include Google.
Papers
More filters
Posted Content
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia,Jessica B. Hamrick,Victor Bapst,Alvaro Sanchez-Gonzalez,Vinicius Zambaldi,Mateusz Malinowski,Andrea Tacchetti,David Raposo,Adam Santoro,Ryan Faulkner,Caglar Gulcehre,H. Francis Song,Andrew J. Ballard,Justin Gilmer,George E. Dahl,Ashish Vaswani,Kelsey R. Allen,Charlie Nash,Victoria Langston,Chris Dyer,Nicolas Heess,Daan Wierstra,Pushmeet Kohli,Matthew Botvinick,Oriol Vinyals,Yujia Li,Razvan Pascanu +26 more
TL;DR: It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.
Proceedings Article
A simple neural network module for relational reasoning
Adam Santoro,David Raposo,David G. T. Barrett,Mateusz Malinowski,Razvan Pascanu,Peter W. Battaglia,Timothy P. Lillicrap +6 more
TL;DR: In this paper, the authors describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning.
Posted Content
A simple neural network module for relational reasoning
Adam Santoro,David Raposo,David G. T. Barrett,Mateusz Malinowski,Razvan Pascanu,Peter W. Battaglia,Timothy P. Lillicrap +6 more
TL;DR: This work shows how a deep learning architecture equipped with an RN module can implicitly discover and learn to reason about entities and their relations.
Journal ArticleDOI
A category-free neural population supports evolving demands during decision-making
TL;DR: This work evaluated rat PPC neurons recorded during multisensory decisions and revealed that the network explored different dimensions during decision and movement, suggesting that a single network of neurons can support the evolving behavioral demands of decision-making.
Posted Content
Relational Deep Reinforcement Learning.
Vinicius Zambaldi,David Raposo,Adam Santoro,Victor Bapst,Yujia Li,Igor Babuschkin,Karl Tuyls,David P. Reichert,Timothy P. Lillicrap,Edward Lockhart,Murray Shanahan,Victoria Langston,Razvan Pascanu,Matthew Botvinick,Oriol Vinyals,Peter W. Battaglia +15 more
TL;DR: This work introduces an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning.