Matt Botvinick

Computer Science · Physical Sciences

9h-index1.3kcitations18works1.02yr avg
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Research Topics

Domain Adaptation and Few-Shot Learning(4), Multimodal Machine Learning Applications(4), Topic Modeling(2), Neural Networks and Applications(2), Reinforcement Learning in Robotics(2)

Publications18 total

AI and the Future of Digital Public Squares
arXiv (Cornell University)·2024· 1 citedOpen Access
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
arXiv (Cornell University)·2024· 1 citedOpen Access
Scaffolding cooperation in human groups with deep reinforcement learning
2022· 5 citedOpen Access
HiP: Hierarchical Perceiver
arXiv (Cornell University)·2022· 3 citedOpen Access
Collaborating with Humans without Human Data
arXiv (Cornell University)·2021· 3 citedOpen Access
A multi-agent reinforcement learning model of reputation and cooperation in human groups
arXiv (Cornell University)·2021· 6 citedOpen Access
Synthetic Returns for Long-Term Credit Assignment
arXiv (Cornell University)·2021· 11 citedOpen Access
Attention over learned object embeddings enables complex visual\n reasoning
arXiv (Cornell University)·2020· 12 citedOpen Access
Attention over learned object embeddings enables complex visual reasoning
arXiv (Cornell University)·2020· 7 citedOpen Access
Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures.
arXiv (Cornell University)·2020· 22 citedOpen Access
Rapid Task-Solving in Novel Environments
arXiv (Cornell University)·2020Open Access
Is coding a relevant metaphor for building AI?
Behavioral and Brain Sciences·2019· 3 cited
Neural scene representation and rendering
Science·2018· 532 cited
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study
arXiv (Cornell University)·2017· 61 citedOpen Access
Generating Interpretable Images with Controllable Structure
2017· 56 cited
Learning to reinforcement learn
arXiv (Cornell University)·2016· 379 citedOpen Access
Learning to Learn without Gradient Descent by Gradient Descent
arXiv (Cornell University)·2016· 162 citedOpen Access
Learning to Learn without Gradient Descent by Gradient Descent
arXiv (Cornell University)·2016· 22 citedOpen Access

Frequent Co-authors

Adam Santoro(7), Felix Hill(4), Timothy Lillicrap(3), Nando de Freitas(3), Xin Ding(3), David Raposo(3), Kevin R. McKee(3), Théophane Weber(2), Oriol Vinyals(2), Joel Z. Leibo(2), Yutian Chen(2), Matthew W. Hoffman(2), Sergio Gómez Colmenarejo(2), Misha Denil(2), Samuel Ritter(2), David G. T. Barrett(2), Malcolm Reynolds(2), Edward Hughes(2), Raphaël Koster(2), Andrea Tacchetti(2)