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2020 – today
- 2023
[c16]Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz, Erin Grant, Andrew M. Saxe, Felix Hill:
The Transient Nature of Emergent In-Context Learning in Transformers. NeurIPS 2023- [c15]Michael Y. Li, Erin Grant, Thomas L. Griffiths:
Gaussian Process Surrogate Models for Neural Networks. UAI 2023: 1241-1252 - [i13]Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Erin Grant, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart
, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew K. Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths:
Getting aligned on representational alignment. CoRR abs/2310.13018 (2023) - [i12]Aaditya K. Singh, Stephanie C. Y. Chan, Ted Moskovitz, Erin Grant, Andrew M. Saxe, Felix Hill:
The Transient Nature of Emergent In-Context Learning in Transformers. CoRR abs/2311.08360 (2023) - [i11]Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy:
Bayes in the age of intelligent machines. CoRR abs/2311.10206 (2023) - 2022
- [b1]Erin Grant:
Cognitive analyses of machine learning systems. University of California, Berkeley, USA, 2022 - [j1]Ben Prystawski, Erin Grant, Aida Nematzadeh, Spike W. S. Lee, Suzanne Stevenson, Yang Xu:
The Emergence of Gender Associations in Child Language Development. Cogn. Sci. 46(6) (2022) - [c14]Ishita Dasgupta, Erin Grant, Tom Griffiths:
Distinguishing rule and exemplar-based generalization in learning systems. ICML 2022: 4816-4830 - [i10]Michael Y. Li, Erin Grant, Thomas L. Griffiths:
Gaussian process surrogate models for neural networks. CoRR abs/2208.06028 (2022) - 2021
- [c13]Shikhar Tuli, Ishita Dasgupta, Erin Grant, Tom Griffiths:
Are Convolutional Neural Networks or Transformers more like human vision? CogSci 2021 - [c12]Thomas A. Langlois, H. Charles Zhao, Erin Grant, Ishita Dasgupta, Thomas L. Griffiths, Nori Jacoby:
Passive attention in artificial neural networks predicts human visual selectivity. NeurIPS 2021: 27094-27106 - [i9]Shikhar Tuli, Ishita Dasgupta, Erin Grant, Thomas L. Griffiths:
Are Convolutional Neural Networks or Transformers more like human vision? CoRR abs/2105.07197 (2021) - [i8]Thomas A. Langlois, H. Charles Zhao, Erin Grant, Ishita Dasgupta, Thomas L. Griffiths, Nori Jacoby:
Passive attention in artificial neural networks predicts human visual selectivity. CoRR abs/2107.07013 (2021) - [i7]Ishita Dasgupta, Erin Grant, Thomas L. Griffiths:
Distinguishing rule- and exemplar-based generalization in learning systems. CoRR abs/2110.04328 (2021) - 2020
- [c11]Richard Thomas McCoy, Erin Grant, Paul Smolensky, Tom Griffiths, Tal Linzen:
Universal linguistic inductive biases via meta-learning. CogSci 2020 - [c10]Ben Prystawski, Erin Grant, Aida Nematzadeh, Spike W. S. Lee, Suzanne Stevenson, Yang Xu:
Tracing the Emergence of Gendered Language in Childhood. CogSci 2020 - [i6]R. Thomas McCoy, Erin Grant, Paul Smolensky, Thomas L. Griffiths, Tal Linzen:
Universal linguistic inductive biases via meta-learning. CoRR abs/2006.16324 (2020) - [i5]Rachit Dubey, Erin Grant, Michael Luo, Karthik Narasimhan, Thomas L. Griffiths:
Context-Conditioning as Cognitive Control: Guiding Meta-learning with Task Information. CoRR abs/2011.13782 (2020)
2010 – 2019
- 2019
- [c9]Erin Grant, Joshua C. Peterson, Tom Griffiths:
Learning deep taxonomic priors for concept learning from few positive examples. CogSci 2019: 1865-1870 - [c8]Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller:
Reconciling meta-learning and continual learning with online mixtures of tasks. NeurIPS 2019: 9119-9130 - 2018
- [c7]Kaylee Burns, Aida Nematzadeh, Erin Grant, Alison Gopnik, Thomas L. Griffiths:
Exploiting Attention to Reveal Shortcomings in Memory Models. BlackboxNLP@EMNLP 2018: 378-380 - [c6]Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths:
Evaluating Theory of Mind in Question Answering. EMNLP 2018: 2392-2400 - [c5]Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths:
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. ICLR (Poster) 2018 - [i4]Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths:
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. CoRR abs/1801.08930 (2018) - [i3]Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths:
Evaluating Theory of Mind in Question Answering. CoRR abs/1808.09352 (2018) - [i2]Ghassen Jerfel, Erin Grant, Thomas L. Griffiths, Katherine A. Heller:
Online gradient-based mixtures for transfer modulation in meta-learning. CoRR abs/1812.06080 (2018) - 2017
- [c4]Erin Grant, Aida Nematzadeh, Thomas L. Griffiths:
How Can Memory-Augmented Neural Networks Pass a False-Belief Task? CogSci 2017 - 2016
- [c3]Erin Grant, Aida Nematzadeh, Suzanne Stevenson:
The Interaction of Memory and Attention in Novel Word Generalization: A Computational Investigation. CogSci 2016 - [i1]Erin Grant, Aida Nematzadeh, Suzanne Stevenson:
The Interaction of Memory and Attention in Novel Word Generalization: A Computational Investigation. CoRR abs/1602.05944 (2016) - 2015
- [c2]Aida Nematzadeh, Erin Grant, Suzanne Stevenson:
A Computational Account of Novel Word Generalization. CogSci 2015 - [c1]Aida Nematzadeh, Erin Grant, Suzanne Stevenson:
A Computational Cognitive Model of Novel Word Generalization. EMNLP 2015: 1795-1804
Coauthor Index
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