David P. Reichert

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Bio

He is interested in universal computational principles of cortical processing and the interface between computational neuroscience and machine learning. Key topics for him are deep neural networks, probabilistic approaches to cognition, learning of generative models, attention, and bridging perception and reasoning.

Research Interests

Papers共 142 篇Author StatisticsCo-AuthorSimilar Experts

By YearBy Citation主题筛选期刊级别筛选合作者筛选合作机构筛选

Can Foundation Models Actively Gather Information in Interactive Environments to Test Hypotheses

Nan Rosemary Ke, Danny P. Sawyer, Hubert Soyer,Martin Engelcke,David P Reichert,Drew A. Hudson, John Reid,Alexander Lerchner,Danilo Jimenez Rezende,Timothy P Lillicrap,Michael Mozer,Jane X Wang

arxiv（2024）

Cited0Views0Bibtex

Scaling Instructable Agents Across Many Simulated Worlds

SIMA Team,Maria Abi Raad,Arun Ahuja, Catarina Barros,Frederic Besse,Andrew Bolt,Adrian Bolton, Bethanie Brownfield,Gavin Buttimore, Max Cant, Sarah Chakera,Stephanie C. Y. Chan,Jeff Clune,Adrian Collister, Vikki Copeman, Alex Cullum,Ishita Dasgupta,Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy,Martin Engelcke,Ryan Faulkner, Frankie Garcia, Charles Gbadamosi,Zhitao Gong, Lucy Gonzales, Kshitij Gupta,Karol Gregor, Arne Olav Hallingstad,Tim Harley, Sam Haves,Felix Hill, Ed Hirst,Drew A. Hudson, Jony Hudson,Steph Hughes-Fitt,Danilo J. Rezende, Mimi Jasarevic, Laura Kampis,Rosemary Ke,Thomas Keck,Junkyung Kim, Oscar Knagg,Kavya Kopparapu, Rory Lawton,Andrew Lampinen,Shane Legg,Alexander Lerchner, Marjorie Limont, Yulan Liu,Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons,Loic Matthey, Siobhan Mcloughlin,Piermaria Mendolicchio,Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valeria Oliveira, Yanko Oliveira, Hannah Openshaw, Renke Pan,Aneesh Pappu,Alex Platonov, Ollie Purkiss,David Reichert, John Reid,Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer,Tim Scholtes, Guy Simmons, Daniel Slater,Hubert Soyer,Heiko Strathmann, Peter Stys,Allison C. Tam,Denis Teplyashin,Tayfun Terzi, Davide Vercelli,Bojan Vujatovic,Marcus Wainwright,Jane X. Wang, Zhengdong Wang,Daan Wierstra, Duncan Williams,Nathaniel Wong,Sarah York, Nick Young

CoRR (2024)

Cited0Views0EIBibtex

Alchemy: A Benchmark and Analysis Toolkit for Meta-Reinforcement Learning Agents

Jane X. Wang,Michael King,Nicolas Porcel,Zeb Kurth-Nelson,Tina Zhu,Charlie Deck,Peter Choy,Mary Cassin,Malcolm Reynolds,Francis Song,Gavin Buttimore,David P. Reichert,Neil Rabinowitz,Loic Matthey,Demis Hassabis,Alexander Lerchner,Matthew Botvinick

Neural Information Processing Systems (2021)

Cited3Views0EIBibtex

Automated Curricula Through Setter-Solver Interactions

Sebastien Racaniere,Andrew K. Lampinen,Adam Santoro,David P. Reichert,Vlad Firoiu,Timothy P. Lillicrap

International Conference on Learning Representations（2020）

Cited61Views0EIBibtex

Learning Dynamic State Abstractions for Model-Based Reinforcement Learning

Lars Buesing,Théophane Weber,Sébastien Racanière,S. M. Ali Eslami,Danilo Jimenez Rezende,David Reichert,Fabio Viola,Frederic Besse,Karol Gregor,Demis Hassabis

openalex（2018）

Cited0Views0Bibtex

Learning and Querying Fast Generative Models for Reinforcement Learning

Lars Buesing,Theophane Weber,Sebastien Racaniere,S. M. Ali Eslami,Danilo Rezende,David P. Reichert,Fabio Viola,Frederic Besse,Karol Gregor,Demis Hassabis,Daan Wierstra

CoRR（2018）

Cited112Views0EIBibtex

112

Relational Deep Reinforcement Learning

Vinícius Flores 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 Battaglia

arXiv: Learning（2018）

Cited292Views0EIBibtex

292

Learning Dynamic State Abstractions for Model Based Reinforcement Learning

Lars Buesing,Theophane Weber,Sebastien Racaniere,SM Ali Eslami,Danilo Rezende,David Reichert,Fabio Viola,Frederic Besse,Karol Gregor,Demis Hassabis,Daan Wierstra

user-5ebe3bbdd0b15254d6c50b2c（2018）

Cited0Views0Bibtex

Deep Reinforcement Learning with Relational Inductive Biases

International Conference on Learning Representations（2018）

Cited245Views0EIBibtex

245

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Research Scientist

Papers共 142 篇Author StatisticsCo-AuthorSimilar Experts