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By Kumar, S. | P
| Campbell, D., Kumar, S., Giallanza, T., Cohen, J. D., & Griffiths, T. L. (2023). Relational constraints on neural networks reproduce human biases towards abstract geometric regularity. (preprint link)
| DMRL
| Kumar, S., Dasgupta, I., Daw, N. D., Cohen, J. D., Griffiths, T. L. (2023) Disentangling abstraction from statistical pattern matching in human and machine learning. PLoS Computational Biology 19(8). (pdf)
| SML
DMRL
| Kumar, S., Correa, C. G., Dasgupta, I., Marjieh, R., Hu, M. Y., Hawkins, R.D., Daw, N. D., Cohen, J. D., Narasimhan, K. R., & Griffiths, T. L. (2022). Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines. Advances in Neural Information Processing Systems, 36. (preprint link)
| DMRL
| Kumar, S., Dasgupta, I., Marjieh, R., Daw, N. D., Cohen, J. D., & Griffiths, T. L. (2022). Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. (preprint link)
| SML
| Kumar, S., Sumers, T. R., Yamakoshi, T., Goldstein, A., Hasson, U., Norman, K. A., Griffiths, T. L., Hawkins, R. D., Nastase, S. A. (2022). Reconstructing the cascade of language processing in the brain using the internal computations of a transformer-based language model. (preprint link)
| DMRL
| Kumar, S., Dasgupta, I., Cohen, J. D., Daw, N. D., & Griffiths, T. L. (2021). Meta-learning of structured task distributions in humans and machines. Proceedings of the 9th International Conference on Learning Representations (ICLR). (pdf)
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