All Publications

2021

S. Yang, Bill, J., Drugowitsch, J., and Gershman, S. J., “Human visual motion perception shows hallmarks of Bayesian structural inference”, Scientific Reports, vol. 11, no. 1, 2021.
CBMM Funded
T. L. Griffiths and Zaslavsky, N., Encyclopedia of Color Science and TechnologyBayesian Approaches to Color Category Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021, pp. 1 - 5.
CBMM Related
M. S. Tomov, Schulz, E., and Gershman, S. J., “Multi-task reinforcement learning in humans”, Nature Human Behaviour, 2021.
CBMM Funded
CBMM Memo No.
131
C. Conwell, Mayo, D., Buice, M. A., Katz, B., Alvarez, G. A., and Barbu, A., “Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex”. 2021.PDF icon CBMM-Memo-131.pdf (9.37 MB)
CBMM Funded
CBMM Memo No.
130
R. Tejwani, Kuo, Y. - L., Shu, T., Katz, B., and Barbu, A., “Social Interactions as Recursive MDPs”. 2021.PDF icon CBMM-Memo-130.pdf (1.52 MB)
CBMM Funded
CBMM Memo No.
129
Y. - L. Kuo, Katz, B., and Barbu, A., “Compositional Networks Enable Systematic Generalization for Grounded Language Understanding”. 2021.PDF icon CBMM-Memo-129.pdf (1.2 MB)
CBMM Funded
CBMM Memo No.
128
I. Palmer, Rouditchenko, A., Barbu, A., Katz, B., and Glass, J., “Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset”. 2021.PDF icon CBMM-Memo-128.pdf (2.91 MB)
CBMM Funded
CBMM Memo No.
127
Y. - L. Kuo, Barbu, A., and Katz, B., “Compositional RL Agents That Follow Language Commands in Temporal Logic”. 2021.PDF icon CBMM-Memo-127.pdf (2.12 MB)
CBMM Funded
CBMM Memo No.
126
C. Ross, Barbu, A., and Katz, B., “Measuring Social Biases in Grounded Vision and Language Embeddings”. 2021.PDF icon CBMM-Memo-126.pdf (1.32 MB)
CBMM Funded
C. Ross, Katz, B., and Barbu, A., “Measuring Social Biases in Grounded Vision and Language Embeddings”, NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics). 2021.
CBMM Funded

2020

W. A. Freiwald, “Social interaction networks in the primate brain”, Current Opinion in Neurobiology, vol. 65, pp. 49 - 58, 2020.
CBMM Funded
M. Nye, Solar-Lezama, A., Tenenbaum, J. B., and Lake, B. M., “Learning Compositional Rules via Neural Program Synthesis”, in Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020), 2020.PDF icon 2003.05562.pdf (2.51 MB)
CBMM Funded
L. Tian, Ellis, K., Kryven, M., and Tenenbaum, J. B., “Learning abstract structure for drawing by efficient motor program induction”, in Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020), 2020.
CBMM Funded
S. - M. Udrescu, Tan, A., Feng, J., Neto, O., Wu, T., and Tegmark, M., “AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity”, in Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020), 2020.PDF icon 2006.10782.pdf (2.62 MB)
CBMM Funded
C. Wang, Ross, C., Kuo, Y. - L., Katz, B., and Barbu, A., “Learning a Natural-language to LTL Executable Semantic Parser for Grounded Robotics”, Proceedings of Conference on Robot Learning (CoRL-2020), 2020.
CBMM Funded
K. Allen, Smith, K. A., and Tenenbaum, J. B., “Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning”, Proceedings of the National Academy of Sciences, p. 201912341, 2020.PDF icon 1912341117.full_.pdf (2.15 MB)
CBMM Funded
CBMM Memo No.
113
A. Banburski, Gandhi, A., Alford, S., Dandekar, S., Chin, P., and Poggio, T., “Dreaming with ARC”, Learning Meets Combinatorial Algorithms workshop at NeurIPS 2020. 2020.PDF icon CBMM Memo 113.pdf (1019.64 KB)
CBMM Funded
CBMM Memo No.
122
C. Wang, Ross, C., Kuo, Y. - L., Katz, B., and Barbu, A., “Learning a natural-language to LTL executable semantic parser for grounded robotics”. 2020.PDF icon CBMM-Memo-122.pdf (1.03 MB)
CBMM Funded
G. Ben-Yosef, Kreiman, G., and Ullman, S., “Minimal videos: Trade-off between spatial and temporal information in human and machine vision.”, Cognition, 2020.
CBMM Funded
E. Schulz, Quiroga, F., and Gershman, S. J., “Communicating Compositional Patterns”, Open Mind, vol. 4, pp. 25 - 39, 2020.
CBMM Funded
Y. - L. Kuo, Katz, B., and Barbu, A., “Deep compositional robotic planners that follow natural language commands ”, in International Conference on Robotics and Automation (ICRA), Palais des Congrès de Paris, Paris, France, 2020.
CBMM Funded
G. Ben-Yosef, Kreiman, G., and Ullman, S., “What can human minimal videos tell us about dynamic recognition models?”, in International Conference on Learning Representations (ICLR 2020), Virtual Conference, 2020.PDF icon Authors' final version (516.09 KB)
CBMM Funded
I. Dasgupta, Schulz, E., Tenenbaum, J. B., and Gershman, S. J., “A theory of learning to infer.”, Psychological Review, vol. 127, no. 3, pp. 412 - 441, 2020.
CBMM Funded
T. Eisape, Levy, R., Tenenbaum, J. B., and Zaslavsky, N., “Toward human-like object naming in artificial neural systems ”, in International Conference on Learning Representations (ICLR 2020), Bridging AI and Cognitive Science workshop, Virtual conference (due to Covid-19), 2020.
CBMM Funded
S. Kim and Spelke, E. S., “Learning from multiple informants: Children’s response to epistemic bases for consensus judgments”, Journal of Experimental Child Psychology, vol. 192, p. 104759, 2020.
CBMM Related

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