Symbolic Compositional Models

In Module 4, we are seeking to understand how complex data and knowledge are represented, manipulated, and learned. We aim to understand how humans perform abstract tasks, some of which are quintessentially human, such as the creation and use of concepts, logical reasoning, mathematics, and language, and long-range planning over long periods. Separate accounts for some of these abilities exist, but they are generally limited by not including perception and learning in real-world settings as well as not sharing knowledge or representation between abilities. Module 4 integrates capabilities from all modules in order to develop models that carry out these larger tasks, such as models that acquire language and allow agents to follow commands. Within Module 4, our projects span low-level perception for the symbols needed for higher-level reasoning, heading toward how to structure image understanding capabilities, expanding those capabilities to include social understanding and culminate in understanding long plans and stories.

Recent Publications

CBMM Funded
W. A. Freiwald, Social interaction networks in the primate brain, Current Opinion in Neurobiology, vol. 65, pp. 49 - 58, 2020.
CBMM Funded
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