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P Perception
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CI Causal Induction
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RPM Rational Process Models
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SML Statistical Models of Language
NBM Nonparametric Bayesian Models
CEIL Cultural Evolution and Iterated Learning
DMRL Decision Making and Reinforcement Learning
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By Narasimhan, K. | 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)
| SML
DMRL
| Kumar, S., Dasgupta, I., Hu, M. Y., Marjieh, R., Hawkins, R. D., Daw, N., Cohen, J., Narasimhan, K. R., & Griffiths, T. L. (2022). Using Natural Language to Guide Meta-Learning Agents towards Human-like Inductive Biases. BACL 1st Workshop on Learning with Natural Language Supervision. (pdf)
| SML
DMRL
| Sumers, T. R., Ho, M. K., Hawkins, R. D., Narasimhan, K. R., & Griffiths, T. L. (2021). Learning rewards from linguistic feedback. Proceedings of the 35th AAAI Conference on Artificial Intelligence. (pdf)
| DMRL
| Dubey, R., Grant, E., Luo, M., Narasimhan, K. R., & Griffiths, T. L. (2020). Connecting context-specific adaptation in humans to meta-learning. (preprint link)
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