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Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks


May 11, 2022
Katherine M. Collins, Catherine Wong, Jiahai Feng, Megan Wei, Joshua B. Tenenbaum

* Originally accepted to the 2022 Cognitive Science (CogSci) conference 

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Identifying concept libraries from language about object structure


May 11, 2022
Catherine Wong, William P. McCarthy, Gabriel Grand, Yoni Friedman, Joshua B. Tenenbaum, Jacob Andreas, Robert D. Hawkins, Judith E. Fan

* Appears in the conference proceedings of CogSci 2022 

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Leveraging Language to Learn Program Abstractions and Search Heuristics


Jun 18, 2021
Catherine Wong, Kevin Ellis, Joshua B. Tenenbaum, Jacob Andreas

* appeared in Thirty-eighth International Conference on Machine Learning (ICML 2021) 

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Communicating Natural Programs to Humans and Machines


Jun 15, 2021
Samuel Acquaviva, Yewen Pu, Marta Kryven, Catherine Wong, Gabrielle E Ecanow, Maxwell Nye, Theodoros Sechopoulos, Michael Henry Tessler, Joshua B. Tenenbaum

* equal contributions: (author 3, 4), (author 5,6,7) 

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DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning


Jun 15, 2020
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum


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Transfer Learning with Neural AutoML


Sep 27, 2018
Catherine Wong, Neil Houlsby, Yifeng Lu, Andrea Gesmundo


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Amanuensis: The Programmer's Apprentice


Jun 29, 2018
Thomas Dean, Maurice Chiang, Marcus Gomez, Nate Gruver, Yousef Hindy, Michelle Lam, Peter Lu, Sophia Sanchez, Rohun Saxena, Michael Smith, Lucy Wang, Catherine Wong


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DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation


Dec 14, 2017
Catherine Wong


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Transfer Learning to Learn with Multitask Neural Model Search


Oct 30, 2017
Catherine Wong, Andrea Gesmundo


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