Publication

Found 230 results
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2023
Gaziv, G., Lee, M. J. & DiCarlo, J. J. Robustified ANNs Reveal Wormholes Between Human Category Percepts. arXiv (2023). at <https://arxiv.org/abs/2308.06887>
Xie, Y., Li, Y. & Rangamani, A. Skip Connections Increase the Capacity of Associative Memories in Variable Binding Mechanisms. (2023).PDF icon CBMM-Memo-142.pdf (1.64 MB)
Gaziv, G., Lee, M. J. & DiCarlo, J. J. Strong and Precise Modulation of Human Percepts via Robustified ANNs. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/hash/d00904cebc0d5b69fada8ad33d0f1422-Abstract-Conference.html>
2022
Guo, C. et al. Adversarially trained neural representations may already be as robust as corresponding biological neural representations. arXiv (2022).
Guo, C. et al. Adversarially trained neural representations may already be as robust as corresponding biological neural representations. arXiv (2022).
Li, Y. et al. An approximate representation of objects underlies physical reasoning. psyArXiv (2022). at <https://psyarxiv.com/vebu5/>
Liu, S. et al. Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents’ Action PlansAbstract. Open Mind 6, 211 - 231 (2022).
Bardon, A., Xiao, W., Ponce, C. R., Livingstone, M. S. & Kreiman, G. Face neurons encode nonsemantic features. Proceedings of the National Academy of Sciences 119, (2022).
Sherman, M. A. et al. Genome-wide mapping of somatic mutation rates uncovers drivers of cancerAbstract. Nature Biotechnology 40, 1634 - 1643 (2022).
Letizia, M. et al. Learning new physics efficiently with nonparametric methodsAbstract. The European Physical Journal C 82, (2022).
Letizia, M. et al. Learning new physics efficiently with nonparametric methodsAbstract. The European Physical Journal C 82, (2022).
Zhang, M. et al. Look twice: A generalist computational model predicts return fixations across tasks and species. PLOS Computational Biology 18, e1010654 (2022).PDF icon journal.pcbi_.1010654.pdf (4.51 MB)
Montagna, F., Noceti, N., Rosasco, L., Zhang, K. & Locatello, F. Scalable Causal Discovery with Score Matching. NeurIPS 2022 (2022). at <https://openreview.net/forum?id=v56PHv_W2A>
Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. (2022).PDF icon CBMM-Memo-132.pdf (1.15 MB)
Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022). doi:10.1109/ICRA46639.2022.9811928
Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
Gjata, N. N., Ullman, T. D., Spelke, E. S. & Liu, S. What Could Go Wrong: Adults and Children Calibrate Predictions and Explanations of Others' Actions Based on Relative Reward and Danger. Cognitive Science 46, (2022).
2021
Shu, T. et al. AGENT: A Benchmark for Core Psychological Reasoning. Proceedings of the 38th International Conference on Machine Learning (2021).
Hu, J., Zaslavsky, N. & Levy, R. Competition from novel features drives scalar inferences in reference games. Proceedings of the Annual Meeting of the Cognitive Science Society 43, (2021).
Xu, M. et al. Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss. (2021).PDF icon v1.0 (4.61 MB)PDF icon v1.4corrections to generalization section (5.85 MB)PDF icon v1.7Small edits (22.65 MB)
Landi, S. M., Viswanathan, P., Serene, S. & Freiwald, W. A. A fast link between face perception and memory in the temporal pole. Science eabi6671 (2021). doi:10.1126/science.abi6671
Ullman, S. et al. Image interpretation by iterative bottom-up top- down processing. (2021).PDF icon CBMM-Memo-120.pdf (2.83 MB)
Anzellottti, S., Houlihan, S. Dae, Liburd, Jr, S. & Saxe, R. Leveraging facial expressions and contextual information to investigate opaque representations of emotions. Emotion (2021). doi:10.1037/emo0000685PDF icon Anzellotti 2021 Emotion.pdf (1.08 MB)

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