Matthew Botvinick

According to our database1, Matthew Botvinick authored at least 42 papers between 2008 and 2018.

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Bibliography

2018
Relational Forward Models for Multi-Agent Learning.
CoRR, 2018

Learning to Share and Hide Intentions using Information Regularization.
CoRR, 2018

Relational Deep Reinforcement Learning.
CoRR, 2018

Relational inductive biases, deep learning, and graph networks.
CoRR, 2018

Been There, Done That: Meta-Learning with Episodic Recall.
CoRR, 2018

Probing Physics Knowledge Using Tools from Developmental Psychology.
CoRR, 2018

Unsupervised Predictive Memory in a Goal-Directed Agent.
CoRR, 2018

On the importance of single directions for generalization.
CoRR, 2018

Machine Theory of Mind.
CoRR, 2018

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents.
CoRR, 2018

Been There, Done That: Meta-Learning with Episodic Recall.
Proceedings of the 35th International Conference on Machine Learning, 2018

Machine Theory of Mind.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Predictive representations can link model-based reinforcement learning to model-free mechanisms.
PLoS Computational Biology, 2017

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017.
CoRR, 2017

Structure Learning in Motor Control: A Deep Reinforcement Learning Model.
CoRR, 2017

Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study.
CoRR, 2017

SCAN: Learning Abstract Hierarchical Compositional Visual Concepts.
CoRR, 2017

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning.
CoRR, 2017

Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study.
Proceedings of the 34th International Conference on Machine Learning, 2017

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning.
Proceedings of the 34th International Conference on Machine Learning, 2017

Learning to Learn without Gradient Descent by Gradient Descent.
Proceedings of the 34th International Conference on Machine Learning, 2017

Structure Learning in Motor Control: A Deep Reinforcement Learning Model.
Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

Tutorial: Recent Advances in Deep Learning.
Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017

2016
Learning to reinforcement learn.
CoRR, 2016

One-shot Learning with Memory-Augmented Neural Networks.
CoRR, 2016

Meta-Learning with Memory-Augmented Neural Networks.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Leveraging Preposition Ambiguity to Assess Compositional Distributional Models of Semantics.
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, 2015

2014
Optimal Behavioral Hierarchy.
PLoS Computational Biology, 2014

The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers.
Cognitive Science, 2014

Design Principles of the Hippocampal Cognitive Map.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013
Neural Representation of Reward Probability: Evidence from the Illusion of Control.
J. Cognitive Neuroscience, 2013

Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments.
Artif. Intell., 2013

Simitar: Simplified Searching of Statistically Significant Similarity Structure.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2013

Structured cognitive representations and complex inference in neural systems.
Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans.
Proceedings of the Computational and Robotic Models of the Hierarchical Organization of Behavior, 2013

2012
Commentary: Why I Am Not a Dynamicist.
topiCS, 2012

A systematic approach to extracting semantic information from functional MRI data.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011
Errors of interpretation and modeling: A reply to Grinband et al.
NeuroImage, 2011

Information mapping with pattern classifiers: A comparative study.
NeuroImage, 2011

Classification of functional magnetic resonance imaging data using informative pattern features.
Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

2009
Machine learning classifiers and fMRI: A tutorial overview.
NeuroImage, 2009

2008
Goal-directed decision making in prefrontal cortex: a computational framework.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008


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