A Computational Account of Novel Word Generalization. - Aida Nematzadeh; Erin Grant; Suzanne Stevenson (CogSci, 2015)

A Computational Cognitive Model of Novel Word Generalization. - Aida Nematzadeh; Erin Grant; Suzanne Stevenson (EMNLP, 2015)

The Interaction of Memory and Attention in Novel Word Generalization: A Computational Investigation. - Erin Grant; Aida Nematzadeh; Suzanne Stevenson (CogSci, 2016)

How Can Memory-Augmented Neural Networks Pass a False-Belief Task? - Erin Grant; Aida Nematzadeh; Thomas L. Griffiths 001 (CogSci, 2017)

Exploiting Attention to Reveal Shortcomings in Memory Models. - Kaylee Burns; Aida Nematzadeh; Erin Grant; Alison Gopnik; Thomas L. Griffiths 001 (BlackboxNLP@EMNLP, 2018)

Evaluating Theory of Mind in Question Answering. - Aida Nematzadeh; Kaylee Burns; Erin Grant; Alison Gopnik; Thomas L. Griffiths 001 (EMNLP, 2018)

Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. - Erin Grant; Chelsea Finn; Sergey Levine; Trevor Darrell; Thomas L. Griffiths 001 (ICLR (Poster), 2018)

Online gradient-based mixtures for transfer modulation in meta-learning. - Ghassen Jerfel; Erin Grant; Thomas L. Griffiths 001; Katherine A. Heller (CoRR, 2018)

Learning deep taxonomic priors for concept learning from few positive examples. - Erin Grant; Joshua C. Peterson; Tom Griffiths 001 (CogSci, 2019)

Reconciling meta-learning and continual learning with online mixtures of tasks. - Ghassen Jerfel; Erin Grant; Tom Griffiths 001; Katherine A. Heller (NeurIPS, 2019)

Universal linguistic inductive biases via meta-learning. - Richard Thomas McCoy; Erin Grant; Paul Smolensky; Tom Griffiths 001; Tal Linzen (CogSci, 2020)

Tracing the Emergence of Gendered Language in Childhood. - Ben Prystawski; Erin Grant; Aida Nematzadeh; Spike W. S. Lee; Suzanne Stevenson; Yang Xu 023 (CogSci, 2020)

Context-Conditioning as Cognitive Control: Guiding Meta-learning with Task Information. - Rachit Dubey; Erin Grant; Michael Luo; Karthik Narasimhan; Thomas L. Griffiths 001 (CoRR, 2020)

Are Convolutional Neural Networks or Transformers more like human vision? - Shikhar Tuli; Ishita Dasgupta 001; Erin Grant; Tom Griffiths 001 (CogSci, 2021)

Passive attention in artificial neural networks predicts human visual selectivity. - Thomas A. Langlois; H. Charles Zhao; Erin Grant; Ishita Dasgupta 001; Thomas L. Griffiths 001; Nori Jacoby (NeurIPS, 2021)

Distinguishing rule and exemplar-based generalization in learning systems. - Ishita Dasgupta 001; Erin Grant; Tom Griffiths 001 (ICML, 2022)

The Emergence of Gender Associations in Child Language Development. - Ben Prystawski; Erin Grant; Aida Nematzadeh; Spike W. S. Lee; Suzanne Stevenson; Yang Xu 023 (Cogn. Sci., 2022)

Cognitive analyses of machine learning systems - Erin Grant (2022)

The Transient Nature of Emergent In-Context Learning in Transformers. - Aaditya K. Singh; Stephanie C. Y. Chan; Ted Moskovitz; Erin Grant; Andrew M. Saxe; Felix Hill (NeurIPS, 2023)

Gaussian Process Surrogate Models for Neural Networks. - Michael Y. Li; Erin Grant; Thomas L. Griffiths 001 (UAI, 2023)

Getting aligned on representational alignment. - 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; et al. (CoRR, 2023)

Bayes in the age of intelligent machines. - Thomas L. Griffiths 001; Jian-Qiao Zhu; Erin Grant; R. Thomas McCoy (CoRR, 2023)

Nonlinear dynamics of localization in neural receptive fields. - Leon Lufkin; Andrew M. Saxe; Erin Grant (NeurIPS, 2024)