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David G. T. Barrett
Person information

- affiliation: Google DeepMind, London, UK
- affiliation: University of Cambridge, Department of Engineering, UK
- affiliation: ENS Paris, Group for Neural Theory, France
- affiliation (PhD 2012): University College London, UK
Other persons with the same name
- David Barrett — disambiguation page
- David Barrett 0002 — Franklin W. Olin College, Needham, MA, USA
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2020 – today
- 2021
- [c14]David G. T. Barrett, Benoit Dherin:
Implicit Gradient Regularization. ICLR 2021 - [c13]Samuel L. Smith, Benoit Dherin, David G. T. Barrett, Soham De:
On the Origin of Implicit Regularization in Stochastic Gradient Descent. ICLR 2021 - [c12]Mihaela Rosca, Yan Wu, Benoit Dherin, David Barrett:
Discretization Drift in Two-Player Games. ICML 2021: 9064-9074 - [i15]Samuel L. Smith, Benoit Dherin, David G. T. Barrett, Soham De:
On the Origin of Implicit Regularization in Stochastic Gradient Descent. CoRR abs/2101.12176 (2021) - [i14]Mihaela Rosca, Yan Wu, Benoit Dherin, David G. T. Barrett:
Discretization Drift in Two-Player Games. CoRR abs/2105.13922 (2021) - [i13]Benoit Dherin, Micheal Munn, David G. T. Barrett:
The Geometric Occam's Razor Implicit in Deep Learning. CoRR abs/2111.15090 (2021) - 2020
- [c11]David Barrett, Felix Hill, Adam Santoro, Jay McClelland:
Cognitive consequences of structured education in a connectionist model of analogical reasoning. CogSci 2020 - [c10]Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David G. T. Barrett, Marta Garnelo:
An Explicitly Relational Neural Network Architecture. ICML 2020: 8593-8603 - [i12]David G. T. Barrett, Benoit Dherin:
Implicit Gradient Regularization. CoRR abs/2009.11162 (2020)
2010 – 2019
- 2019
- [c9]Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap:
Learning to Make Analogies by Contrasting Abstract Relational Structure. ICLR (Poster) 2019 - [c8]David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld:
Spectral Inference Networks: Unifying Deep and Spectral Learning. ICLR (Poster) 2019 - [i11]Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap:
Learning to Make Analogies by Contrasting Abstract Relational Structure. CoRR abs/1902.00120 (2019) - [i10]Adam Santoro, Felix Hill, David G. T. Barrett, David Raposo, Matthew M. Botvinick, Timothy P. Lillicrap:
Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette. CoRR abs/1904.10396 (2019) - [i9]Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David Barrett, Marta Garnelo:
An Explicitly Relational Neural Network Architecture. CoRR abs/1905.10307 (2019) - 2018
- [c7]Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew M. Botvinick:
On the importance of single directions for generalization. ICLR (Poster) 2018 - [c6]Adam Santoro, Felix Hill, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap:
Measuring abstract reasoning in neural networks. ICML 2018: 4477-4486 - [i8]Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew M. Botvinick:
On the importance of single directions for generalization. CoRR abs/1803.06959 (2018) - [i7]David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld:
Spectral Inference Networks: Unifying Spectral Methods With Deep Learning. CoRR abs/1806.02215 (2018) - [i6]David G. T. Barrett, Felix Hill, Adam Santoro, Ari S. Morcos, Timothy P. Lillicrap:
Measuring abstract reasoning in neural networks. CoRR abs/1807.04225 (2018) - [i5]David G. T. Barrett, Ari S. Morcos, Jakob H. Macke:
Analyzing biological and artificial neural networks: challenges with opportunities for synergy? CoRR abs/1810.13373 (2018) - 2017
- [c5]David Raposo, Adam Santoro, David G. T. Barrett, Razvan Pascanu, Tim Lillicrap, Peter W. Battaglia:
Discovering objects and their relations from entangled scene representations. ICLR (Workshop) 2017 - [c4]Samuel Ritter, David G. T. Barrett, Adam Santoro, Matt M. Botvinick:
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. ICML 2017: 2940-2949 - [c3]Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter W. Battaglia, Tim Lillicrap:
A simple neural network module for relational reasoning. NIPS 2017: 4967-4976 - [i4]David Raposo, Adam Santoro, David G. T. Barrett, Razvan Pascanu, Timothy P. Lillicrap, Peter W. Battaglia:
Discovering objects and their relations from entangled scene representations. CoRR abs/1702.05068 (2017) - [i3]Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter W. Battaglia, Timothy P. Lillicrap:
A simple neural network module for relational reasoning. CoRR abs/1706.01427 (2017) - [i2]Samuel Ritter, David G. T. Barrett, Adam Santoro, Matthew M. Botvinick:
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. CoRR abs/1706.08606 (2017) - [i1]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:
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR abs/1711.08378 (2017) - 2013
- [c2]David G. T. Barrett, Sophie Denève, Christian K. Machens:
Firing rate predictions in optimal balanced networks. NIPS 2013: 1538-1546 - 2012
- [c1]Ralph Bourdoukan, David G. T. Barrett, Christian K. Machens, Sophie Denève:
Learning optimal spike-based representations. NIPS 2012: 2294-2302
Coauthor Index

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