PREPRINTS

Tartaglini A. R., Feucht S., Lepori M. A., Vong W. K., Lovering C., Lake B. M., Pavlick E. (2023). Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations. arXiv preprint arXiv:2310.09612.

PUBLICATIONS

Vong, W. K., Wang, W., Orhan, A. E., & Lake, B. M (2024). Grounded language acquisition through the eyes and ears of a single child. Science. [Code and Models]

Wang, W., Vong, W. K., Kim, N., & Lake, B. M. (2023). Finding Structure in One Child’s Linguistic Experience. Cognitive Science.

Ji, A., Kojima, N., Rush, N., Suhr, A., Vong, W. K., Hawkins, R. D., and Artzi, Y. (2022). Abstract visual reasoning with tangram shapes. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). Best Long Paper Award. [Code and Data] [Models] [Visualizations]

Vong, W. K., & Lake, B. M. (2022). Few-shot image classification by generating natural language rules. First Workshop on Learning from Natural Language Supervision @ ACL 2022.

Radulescu, A., Vong, W. K., & Gureckis, T. M. (2022). Name that state: How language affects human reinforcement learning. Proceedings of the 44th Annual Conference of the Cognitive Science Society.

Tartaglini, A. R., Vong, W. K., & Lake, B. M. (2022). A developmentally-inspired examination of shape versus texture bias in machines. Proceedings of the 44th Annual Conference of the Cognitive Science Society. [Data]

Vong, W. K., & Lake, B. M. (2022). Cross-situational word learning with multimodal neural networks. Cognitive Science. [Code and Data]

Bass, I., Bonawitz, E., Hawthorne, D., Vong, W. K., Goodman, N. D., & Gweon, H. (2022). The effects of information utility and teachers’ knowledge on evaluations of under-informative pedagogy across development. Cognition. [Data]

Johnson, A., Vong, W. K., Lake, B. M., & Gureckis, T. M. (2021). Fast and flexible: Human program induction in abstract reasoning tasks. Proceedings of the 43rd Annual Conference of the Cognitive Science Society. [Visualizations]

Tartaglini, A. R., Vong, W. K., & Lake, B. M. (2021). Modeling artificial category learning from pixels: Revisiting Shepard, Hovland, and Jenkins (1961) with deep neural networks. Proceedings of the 43rd Annual Conference of the Cognitive Science Society.

Yang, S. C. H., Vong, W. K., Sojitra, R. B., Folke, T., & Shafto, P. (2021). Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching. Scientific Reports. [Data and Code]

Vong, W. K., Hendrickson, A. T., Navarro, D. J. & Perfors, A. F. (2019). Do additional features help or harm during category learning? The curse of dimensionality in human learners. Cognitive Science. [Code] [Experiments]

Yang, S.C-H.*, Vong, W.K*., Yu, Y. & Shafto, P. (2019). A unifying computational framework for teaching and active learning. Topics in Cognitive Science. [Code] (* indicates equal contribution)

Vong, W. K.*;, Sojitra, R.*;, Reyes, A., Yang, S. C-H & Shafto, P. (2018). Bayesian teaching of image categories. Proceedings of the 40th Annual Conference of the Cognitive Science Society. (* indicates equal contribution)

Yang, S.C-H., Yu, Y., Givchi, A., Wang, P., Vong, W.K., & Shafto, P. (2018) Optimal Cooperative Inference. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS).

Vong, W.K, Perfors, A. & Navarro, D. J. (2016). The helpfulness of labels in semi-supervised learning depends on category structure. Psychonomic Bulletin & Review. 23(1), 230-238.

Vong, W. K., Hendrickson, A. T., Perfors, A. F. & Navarro, D. J. (2016). Do additional features help or harm during category learning? An exploration of the curse of dimensionality in human learners. Proceedings of the 38th Annual Conference of the Cognitive Science Society. Marr Prize for Best Student Paper.

Vong, W.K, Perfors, A. & Navarro, D. J. (2014). The relevance of labels in semi-supervised learning depends on category structure. Proceedings of the 36th Annual Conference of the Cognitive Science Society.

Vong, W. K., Hendrickson, A. T., Perfors, A. & Navarro, D. J. (2013). The role of sampling assumptions in generalization with multiple categories. Proceedings of the 35th Annual Conference of the Cognitive Science Society.

Navarro, D. J., Perfors, A. & Vong, W. K. (2013). Learning time-varying categories. Memory & Cognition, 41(6), 917-927.