Publications
(* indicates equal contribution)
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Jedoui, K., Venkatesh, R., Wang, H., O’Connell, T., Bai, Y., Tenenbaum, J., Fan, J., Smith, K., and Yamins, D. (2024). Towards task-appropriate readout mechanisms for physical scene understanding. Cognitive Computational Neuroscience. pdf
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Wang, H., Jedoui, K., Venkatesh, R., Binder, F., Tenenbaum, J., Fan, J., Yamins, D., and Smith, K. (2024). Probabilistic simulation supports generalizable intuitive physics. Proceedings of the 46th Annual Meeting of the Cognitive Science Society. pdf
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Martinez, J., Binder, F., Wang, H., Haber, N., Fan, J., and Yamins, D. (2023). Measuring and Modeling Physical Intrinsic Motivation. Proceedings of the 45th Annual Meeting of the Cognitive Science Society. pdf
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Wang, H., Allen, K., Vul, E., and Fan, J. (2022). Generalizing physical prediction by composing forces and objects. Proceedings of the 44th Annual Meeting of the Cognitive Science Society. pdf
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Wang, H., Yang, J., Tamari, R., and Fan, J. (2022). Communicating understanding of physical dynamics in natural language. Proceedings of the 44th Annual Meeting of the Cognitive Science Society. pdf
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Brockbank*, E., Wang*, H., Yang, J., Mirchandani, S., Biyik, E., Sadigh, D., and Fan, J. (2022). How do people incorporate advice from artificial agents when making physical judgments? Proceedings of the 44th Annual Meeting of the Cognitive Science Society. pdf
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Wang, H., Polikarpova, N., and Fan, J. (2021). Learning part-based abstractions for visual object concepts. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. pdf
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Wang, H., Vul, E., Polikarpova, N., and Fan, J. (2021). Theory acquisition as constraint-based program synthesis. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. pdf
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McCarthy*, W., Hawkins*, R., Wang, H., Holdaway, C., and Fan, J. (2021). Learning to communicate about shared procedural abstractions. Proceedings of the 43rd Annual Meeting of the Cognitive Science Society. pdf
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Wang, H., and Fan, J. (2020). Library learning for structured object concepts. ICML Workshop on Object-Oriented Learning: Perception, Representation, and Reasoning. pdf