Erin Grant

Publication Activity (10 Years)

Years Active: 2015-2023
Publications (10 Years): 28

Top Topics

Learning Systems

Positive Examples

Top Venues

Publications

Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy
Bayes in the age of intelligent machines. CoRR (2023)
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)
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 (2023)
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 (2023)
Michael Y. Li, Erin Grant, Thomas L. Griffiths
Gaussian Process Surrogate Models for Neural Networks. UAI (2023)
Michael Y. Li, Erin Grant, Thomas L. Griffiths
Gaussian process surrogate models for neural networks. CoRR (2022)
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)
Ishita Dasgupta, Erin Grant, Tom Griffiths
Distinguishing rule and exemplar-based generalization in learning systems. ICML (2022)
Ishita Dasgupta, Erin Grant, Thomas L. Griffiths
Distinguishing rule- and exemplar-based generalization in learning systems. CoRR (2021)
Shikhar Tuli, Ishita Dasgupta, Erin Grant, Thomas L. Griffiths
Are Convolutional Neural Networks or Transformers more like human vision? CoRR (2021)
Shikhar Tuli, Ishita Dasgupta, Erin Grant, Tom Griffiths
Are Convolutional Neural Networks or Transformers more like human vision? CogSci (2021)
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)
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 (2021)
Richard Thomas McCoy, Erin Grant, Paul Smolensky, Tom Griffiths, Tal Linzen
Universal linguistic inductive biases via meta-learning. CogSci (2020)
Rachit Dubey, Erin Grant, Michael Luo, Karthik Narasimhan, Thomas L. Griffiths
Context-Conditioning as Cognitive Control: Guiding Meta-learning with Task Information. CoRR (2020)
R. Thomas McCoy, Erin Grant, Paul Smolensky, Thomas L. Griffiths, Tal Linzen
Universal linguistic inductive biases via meta-learning. CoRR (2020)
Ben Prystawski, Erin Grant, Aida Nematzadeh, Spike W. S. Lee, Suzanne Stevenson, Yang Xu
Tracing the Emergence of Gendered Language in Childhood. CogSci (2020)
Erin Grant, Joshua C. Peterson, Tom Griffiths
Learning deep taxonomic priors for concept learning from few positive examples. CogSci (2019)
Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller
Reconciling meta-learning and continual learning with online mixtures of tasks. NeurIPS (2019)
Kaylee Burns, Aida Nematzadeh, Erin Grant, Alison Gopnik, Thomas L. Griffiths
Exploiting Attention to Reveal Shortcomings in Memory Models. BlackboxNLP@EMNLP (2018)
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. ICLR (Poster) (2018)
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. CoRR (2018)
Ghassen Jerfel, Erin Grant, Thomas L. Griffiths, Katherine A. Heller
Online gradient-based mixtures for transfer modulation in meta-learning. CoRR (2018)
Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths
Evaluating Theory of Mind in Question Answering. CoRR (2018)
Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths
Evaluating Theory of Mind in Question Answering. EMNLP (2018)
Erin Grant, Aida Nematzadeh, Thomas L. Griffiths
How Can Memory-Augmented Neural Networks Pass a False-Belief Task? CogSci (2017)
Erin Grant, Aida Nematzadeh, Suzanne Stevenson
The Interaction of Memory and Attention in Novel Word Generalization: A Computational Investigation. CogSci (2016)
Erin Grant, Aida Nematzadeh, Suzanne Stevenson
The Interaction of Memory and Attention in Novel Word Generalization: A Computational Investigation. CoRR (2016)
Aida Nematzadeh, Erin Grant, Suzanne Stevenson
A Computational Cognitive Model of Novel Word Generalization. EMNLP (2015)
Aida Nematzadeh, Erin Grant, Suzanne Stevenson
A Computational Account of Novel Word Generalization. CogSci (2015)