Publication

Found 35 results
Author Title [ Type(Asc)] Year
Filters: Author is Samuel J Gershman  [Clear All Filters]
Journal Article
Li, Y. et al. An approximate representation of objects underlies physical reasoning. psyArXiv (2022). at <https://psyarxiv.com/vebu5/>
Dasgupta, I., Guo, D., Gershman, S. J. & Goodman, N. D. Analyzing Machine‐Learned Representations: A Natural Language Case Study. Cognitive Science 44, (2020).
Conference Poster
Kool, W., Gershman, S. J. & Cushman, F. A. Thinking fast or slow? A reinforcement-learning approach. Society for Personality and Social Psychology (2017).PDF icon KoolEtAl_SPSP_2017.pdf (670.35 KB)
Conference Paper
Yu, C., Burgess, N., Sahani, M. & Gershman, S. J. Successor-Predecessor Intrinsic Exploration. Advances in Neural Information Processing Systems 36 (NeurIPS 2023) (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/hash/e6f2b968c4ee8ba260cd7077e39590dd-Abstract-Conference.html>
Tsividis, P., Pouncy, T., Xu, J. L., Tenenbaum, J. B. & Gershman, S. J. Human Learning in Atari. AAAI Spring Symposium Series (2017).PDF icon Tsividis et al - Human Learning in Atari.pdf (844.47 KB)
Liu, S., Cushman, F. A., Gershman, S. J., Kool, W. & Spelke, E. S. Hard choices: Children’s understanding of the cost of action selection. . Cognitive Science Society (2019).PDF icon phk_cogsci_2019_final.pdf (276.14 KB)
CBMM Memos
Dasgupta, I., Schulz, E. & Gershman, S. J. Where do hypotheses come from?. (2016).PDF icon CBMM-Memo-056-v2.pdf (733.35 KB)
Schulz, E., Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M. & Gershman, S. J. Probing the compositionality of intuitive functions. (2016).PDF icon CBMM-Memo-048.pdf (815.72 KB)
Sanders, H., Wilson, M. A. & Gershman, S. J. Hippocampal Remapping as Hidden State Inference. (2019). doi:https://doi.org/10.1101/743260PDF icon CBMM-Memo-101.pdf (12.78 MB)
Lake, B. M., Ullman, T., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. (2016).PDF icon machines_that_think.pdf (3.45 MB)

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