David G. T. Barrett

According to our database, David G. T. Barrett authored at least 22 papers between 2012 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation.

Ryutaro Tanno, David G. T. Barrett, Andrew Sellergren, Sumedh Ghaisas, Sumanth Dathathri, Abigail See, Johannes Welbl, Karan Singhal, Shekoofeh Azizi, Tao Tu, Mike Schaekermann, Rhys May, Roy Lee, SiWai Man, Zahra Ahmed, S. Sara Mahdavi, Danielle Belgrave, Vivek Natarajan, Shravya Shetty, Pushmeet Kohli, Po-Sen Huang, Alan Karthikesalingam, Ira Ktena

CoRR, 2023

DiscoGen: Learning to Discover Gene Regulatory Networks.

Nan Rosemary Ke, Sara-Jane Dunn, Jörg Bornschein, Silvia Chiappa, Mélanie Rey, Jean-Baptiste Lespiau, Albin Cassirer, Jane X. Wang, Theophane Weber, David G. T. Barrett, Matthew M. Botvinick, Anirudh Goyal, Michael Mozer, Danilo J. Rezende

CoRR, 2023

2022

Why neural networks find simple solutions: The many regularizers of geometric complexity.

Benoit Dherin, Michael Munn, Mihaela Rosca, David Barrett

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

The Geometric Occam's Razor Implicit in Deep Learning.

Benoit Dherin, Michael Munn, David G. T. Barrett

CoRR, 2021

Discretization Drift in Two-Player Games.

Mihaela Rosca, Yan Wu, Benoit Dherin, David Barrett

Proceedings of the 38th International Conference on Machine Learning, 2021

On the Origin of Implicit Regularization in Stochastic Gradient Descent.

Samuel L. Smith, Benoit Dherin, David G. T. Barrett, Soham De

Proceedings of the 9th International Conference on Learning Representations, 2021

Implicit Gradient Regularization.

David G. T. Barrett, Benoit Dherin

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

An Explicitly Relational Neural Network Architecture.

Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David G. T. Barrett, Marta Garnelo

Proceedings of the 37th International Conference on Machine Learning, 2020

Cognitive consequences of structured education in a connectionist model of analogical reasoning.

David Barrett, Felix Hill, Adam Santoro, Jay McClelland

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019

Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette.

Adam Santoro, Felix Hill, David G. T. Barrett, David Raposo, Matthew M. Botvinick, Timothy P. Lillicrap

CoRR, 2019

Spectral Inference Networks: Unifying Deep and Spectral Learning.

David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld

Proceedings of the 7th International Conference on Learning Representations, 2019

Learning to Make Analogies by Contrasting Abstract Relational Structure.

Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Analyzing biological and artificial neural networks: challenges with opportunities for synergy?

David G. T. Barrett, Ari S. Morcos, Jakob H. Macke

CoRR, 2018

Spectral Inference Networks: Unifying Spectral Methods With Deep Learning.

David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld

CoRR, 2018

Measuring abstract reasoning in neural networks.

Adam Santoro, Felix Hill, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap

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

On the importance of single directions for generalization.

Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew M. Botvinick

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

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

Matthew M. Botvinick, David G. T. Barrett, Peter W. Battaglia, Nando de Freitas, Dharshan Kumaran, Joel Z. Leibo, Tim Lillicrap, Joseph Modayil, S. Mohamed, Neil C. Rabinowitz, Danilo Jimenez Rezende, Adam Santoro, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis

CoRR, 2017

A simple neural network module for relational reasoning.

Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter W. Battaglia, Tim Lillicrap

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

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

Samuel Ritter, David G. T. Barrett, Adam Santoro, Matt M. Botvinick

Proceedings of the 34th International Conference on Machine Learning, 2017

Discovering objects and their relations from entangled scene representations.

David Raposo, Adam Santoro, David G. T. Barrett, Razvan Pascanu, Tim Lillicrap, Peter W. Battaglia

Proceedings of the 5th International Conference on Learning Representations, 2017

2013

Firing rate predictions in optimal balanced networks.

David G. T. Barrett, Sophie Denève, Christian K. Machens

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Learning optimal spike-based representations.

Ralph Bourdoukan, David G. T. Barrett, Christian K. Machens, Sophie Denève

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