“Nonlinear dynamics of localization in neural receptive fields.”
Leon Lufkin, Andrew Saxe, Erin Grant.
In Advances in Neural Information Processing Systems (NeurIPS), 2024.
Spotlight presentation (<4% of submissions).
“Statistical physics, Bayesian inference and neural information processing.”
Erin Grant, Sandra Nestler, Berfin Şimşek, Sara Solla.
In Journal of Statistical Mechanics: Theory and Experiment, 2024.
Bayes in the age of intelligent machines.
“Bayes in the age of intelligent machines.”
Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy.
In Current Directions in Psychological Science 33: 5, 2024.
The transient nature of emergent in-context learning in transformers.
“The transient nature of emergent in-context learning in transformers.”
Aaditya K. Singh*, Stephanie C.Y. Chan*, Ted Moskovitz, Erin Grant, Andrew Saxe, Felix Hill.
In Advances in Neural Information Processing Systems (NeurIPS), 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, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nori Jacoby, Qiuyi (Richard) 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.
arXiv: 2310.13018.
“Bayesian models and neural networks.”
Thomas L. Griffiths, Ishita Dasgupta, Erin Grant.
In Bayesian models of cognition: Reverse engineering the mind, edited by Thomas L. Griffiths, Nick Chater, and Joshua B. Tenenbaum. Cambridge, MA: MIT Press, 2023.
Gaussian process surrogate models for neural networks.
“Gaussian process surrogate models for neural networks.”
Michael Y. Li, Erin Grant, Thomas L. Griffiths.
In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
Predicting generalization with degrees of freedom in neural networks.
“Predicting generalization with degrees of freedom in neural networks.”
Erin Grant*, Yan Wu*.
In Proceedings of the ICML 2022 2nd AI for Science Workshop (AI4Science), 2022.
Distinguishing rule- and exemplar-based generalization in learning systems.
“Distinguishing rule- and exemplar-based generalization in learning systems.”
Ishita Dasgupta*, Erin Grant*, Thomas L. Griffiths.
In Proceedings of the International Conference on Machine Learning (ICML), 2022.
The emergence of gender associations in child language development.
“The emergence of gender associations in child language development.”
Ben Prystawski, Erin Grant, Aida Nematzadeh, Spike W.S. Lee, Suzanne Stevenson, Yang Xu.
In Cognitive Science, 46: e13146, 2022.
Passive attention in artificial neural networks predicts human visual selectivity.
“Passive attention in artificial neural networks predicts human visual selectivity.”
Thomas A. Langlois*, Charles Zhao*, Erin Grant, Ishita Dasgupta, Thomas L. Griffiths, Nori Jacoby.
In Advances in Neural Information Processing Systems (NeurIPS), 2021.
Oral presentation (<1% of submissions).
Are convolutional neural networks or transformers more like human vision?
“Are convolutional neural networks or transformers more like human vision?”
Shikhar Tuli, Ishita Dasgupta, Erin Grant, Thomas L. Griffiths.
In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci), 2021.
Tracing the emergence of gendered language in childhood.
“Tracing the emergence of gendered language in childhood.”
Ben Prystawski, Erin Grant, Aida Nematzadeh, Spike W.S. Lee, Suzanne Stevenson, Yang Xu.
In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci), 2020.
Universal linguistic inductive biases via meta-learning.
“Universal linguistic inductive biases via meta-learning.”
R. Thomas McCoy, Erin Grant, Paul Smolensky, Thomas L. Griffiths, Tal Linzen.
In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci), 2020.
Oral presentation.
Generalization in planning by metalearning to metareason.
“Generalization in planning by metalearning to metareason.”
Frederick Callaway, Bas van Opheusden, Erin Grant, Thomas L. Griffiths.
In Proceedings of the AAAI Workshop on Generalization in Planning (GenPlan), 2020.
Oral presentation.
Reconciling meta-learning and continual learning with online mixtures of tasks.
“Reconciling meta-learning and continual learning with online mixtures of tasks.”
Ghassen Jerfel*, Erin Grant*, Thomas L. Griffiths, Katherine Heller.
In Advances in Neural Information Processing Systems (NeurIPS), 2019.
Spotlight presentation (<2.5% of submissions).
Doing more with less: Meta-reasoning and meta-learning in humans and machines.
“Doing more with less: Meta-reasoning and meta-learning in humans and machines.”
Thomas L. Griffiths, Frederick Callaway, Michael B. Chang, Erin Grant, Paul M. Krueger, Falk Lieder.
In Current Opinion in Behavioral Sciences 29, 2019.
Learning deep taxonomic priors for concept learning from few positive examples.
“Learning deep taxonomic priors for concept learning from few positive examples.”
Erin Grant, Joshua C. Peterson, Thomas L. Griffiths.
In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci), 2019.
“Exploiting attention to reveal shortcomings in memory models.”
Kaylee Burns, Aida Nematzadeh, Erin Grant, Alison Gopnik, Thomas L. Griffiths.
In Proceedings of the EMNLP Workshop BlackboxNLP: Analyzing & Interpreting Neural Networks for NLP (BlackboxNLP), 2018.
Evaluating theory of mind in question answering.
“Evaluating theory of mind in question answering.”
Aida Nematzadeh, Kaylee Burns, Erin Grant, Alison Gopnik, Thomas L. Griffiths.
In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
Long talk.
Recasting gradient-based meta-learning as hierarchical Bayes.
“Recasting gradient-based meta-learning as hierarchical Bayes.”
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas L. Griffiths.
In Proceedings of the International Conference on Learning Representations (ICLR), 2018.
How can memory-augmented neural networks pass a false-belief task?
“How can memory-augmented neural networks pass a false-belief task?”
Erin Grant, Aida Nematzadeh, Thomas L. Griffiths.
In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci), 2017.
Oral presentation.
The interaction of memory and attention in novel word generalization: A computational investigation.
“The interaction of memory and attention in novel word generalization: A computational investigation.”
Erin Grant, Aida Nematzadeh, Suzanne Stevenson.
In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci), 2016.
Oral presentation.
A computational cognitive model of novel word generalization.
“A computational cognitive model of novel word generalization.”
Aida Nematzadeh, Erin Grant, Suzanne Stevenson.
In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015.
Oral presentation.