Publications
Loose LIPS Sink Ships: Asking Questions in Battleship with Language-Informed Program Sampling.
Gabriel Grand, Valerio Pepe, Jacob Andreas, Joshua B. Tenenbaum. ArXiV: 2402.19471 (2024).
[arXiv]
[GitHub]
LILO: Learning Interpretable Libraries by Compressing and Documenting Code.
Gabriel Grand, Lionel Wong, Matthew Bowers, Theo X. Olausson, Muxin Liu, Joshua B. Tenenbaum, Jacob Andreas. ICLR (2024).
[arXiv]
[GitHub]
Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs.
Alexander K Lew, Tan Zhi-Xuan, Gabriel Grand, Vikash K Mansinghka. ArXiV: 2306.03081 (2023).
[arXiv]
[GitHub]
[Docs]
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought.
Lionel Wong*, Gabriel Grand*, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum. ArXiV: 2306.12672 (2023).
[arXiv]
[GitHub]
Evaluating Statistical Language Models as Pragmatic Reasoners.
Benjamin Lipkin, Lionel Wong, Gabriel Grand, Joshua B. Tenenbaum. Proceedings of the Annual Meeting of the Cognitive Science Society (2023).
[CogSci]
[arXiv]
[GitHub]
Grounded Physical Language Understanding with Probabilistic Programs and Simulated Worlds
Cedegao Zhang, Lionel Wong, Gabriel Grand, Joshua B. Tenenbaum. Proceedings of the Annual Meeting of the Cognitive Science Society (2023).
[CogSci]
[GitHub]
Top-Down Synthesis for Library Learning.
Matthew Bowers, Theo X. Olausson, Lionel Wong, Gabriel Grand, Joshua B. Tenenbaum, Kevin Ellis, Armando Solar-Lezama. Proceedings of the ACM on Programming Languages, Volume 7, Issue POPL (Jan 2023).
[ACM Digital Library]
[arXiv]
[GitHub]
“Semantic projection” recovers rich human knowledge of multiple object features from word embeddings.
Gabriel Grand, Idan Blank, Francisco Pereira, and Evelina Fedorenko. Nature Human Behaviour (2022).
[Nature]
[MIT McGovern Institute]
[arXiv]
Identifying concept libraries from language about object structure.
Catherine Wong*, William P. McCarthy*, Gabriel Grand*, Yoni Friedman, Joshua B. Tenenbaum, Jacob Andreas, Robert D. Hawkins, and Judith E. Fan. CogSci (2022).
[arXiv]
[website]
[GitHub]
ChemBERTa-2: Towards Chemical Foundation Models.
Walid Ahmad, Elana Simon, Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. ELLIS ML for Molecules Worshop (December, 2021).
[paper]
[workshop]
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. NeurIPS ML for Molecules Worshop (December, 2020).
[arXiv]
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects. Gabriel Grand and Yonatan Belinkov. Proceedings of the 2nd Workshop on Shortcomings in Vision and Language (SiVL) at NAACL-HLT, Minneapolis, MN (June, 2019).
[arXiv]
[ACL]
Best Paper Award, SiVL Workshop, NAACL 2019
Learning Interpretable and Bias-Free Models for Visual Question Answering. Gabriel Grand. Harvard Undergraduate Thesis, advised by Alexander Rush and presented to the Department of Computer Science (2018).
[PDF]
Hoopes Prize
On the Flip Side: Identifying Counterexamples in Visual Question Answering. Gabriel Grand, Aron Szanto, Yoon Kim, and Alexander Rush. KDD Deep Learning Day, London, UK (August, 2018).
[arXiv]
[KDD]