Publication

Found 55 results
Author Title Type [ Year(Desc)]
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2017
Spokes, A. C., Howard, R., Mehr, S. A. & Krasnow, M. M. Like Adults, children make consistent welfare tradeoff allocations. Budapest CEU Conference on Cognitive Development (2017).
Telenczuk, B. et al. Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex. Nature Scientific Reports (2017). doi:10.1038/srep40211PDF icon srep40211.pdf (2.53 MB)
Scott, K. M. & Schulz, L. Lookit (Part 1): a new online platform for developmental research. Open Mind 1, (2017).PDF icon UNCORRECTED PROOF (561.21 KB)
Scott, K. M., Chu, J. & Schulz, L. Lookit (Part 2): Assessing the viability of online developmental research, Results from three case studies. Open Mind 1, (2017).PDF icon lookitpart2.pdf (464.02 KB)
Mlynarski, W. & McDermott, J. H. Lossy Compression of Uninformative Stimuli in the Auditory System. Association for Otolaryngology Mid-Winter Meeting (2017).
2018
O'Brien, N., Latessa, S., Evangelopoulos, G. & Boix, X. The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors. workshop on "AI for Social Good", NIPS 2018 (2018). at <http://hdl.handle.net/1721.1/120056>PDF icon fake-news-paper-NIPS.pdf (147.36 KB)PDF icon fake-news-paper-NIPS_2018_v2.pdf (147.36 KB)
Mlynarski, W. & McDermott, J. H. Learning Mid-Level Auditory Codes from Natural Sound Statistics. Neural Computation 30, 631-669 (2018).
Ullman, T. D., Stuhlmüller, A., Goodman, N. D. & Tenenbaum, J. B. Learning physical parameters from dynamic scenes. Cognitive Psychology 104, 57-82 (2018).PDF icon T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)
Wu, K., Wu, E. & Kreiman, G. Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition. arXiv | Cornell University arXiv:1803.01967, (2018).
Eric, W., Kevin, W. & Kreiman, G. Learning scene gist with convolutional neural networks to improve object recognition. 2018 52nd Annual Conference on Information Sciences and Systems (CISS) (2018). doi:10.1109/CISS.2018.8362305PDF icon 08362305.pdf (3.17 MB)
Gerstenberg, T. et al. Lucky or clever? From changed expectations to attributions of responsibility. Cognition (2018).
2019
Calero, C. I., Shalom, D. E., Spelke, E. S. & Sigman, M. Language, gesture, and judgment: Children’s paths to abstract geometry. Journal of Experimental Child Psychology 177, 70 - 85 (2019).
Jozwik, K. M., Lee, M., Marques, T., Schrimpf, M. & Bashivan, P. Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge. The Algonauts Project: Explaining the Human Visual Brain Workshop 2019 (2019). doi:10.1101/689844
Ross, C., Berzak, Y., Katz, B. & Barbu, A. Learning Language from Vision. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS) (2019).
2020
Wang, C., Ross, C., Kuo, Y. - L., Katz, B. & Barbu, A. Learning a Natural-language to LTL Executable Semantic Parser for Grounded Robotics. (Proceedings of Conference on Robot Learning (CoRL-2020), 2020). at <https://corlconf.github.io/paper_385/>
Wang, C., Ross, C., Kuo, Y. - L., Katz, B. & Barbu, A. Learning a natural-language to LTL executable semantic parser for grounded robotics. (2020). doi:https://doi.org/10.48550/arXiv.2008.03277PDF icon CBMM-Memo-122.pdf (1.03 MB)
Tian, L., Ellis, K., Kryven, M. & Tenenbaum, J. B. Learning abstract structure for drawing by efficient motor program induction. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). at <https://papers.nips.cc/paper/2020/hash/1c104b9c0accfca52ef21728eaf01453-Abstract.html>
Nye, M., Solar-Lezama, A., Tenenbaum, J. B. & Lake, B. M. Learning Compositional Rules via Neural Program Synthesis. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). at <https://proceedings.neurips.cc/paper/2020/hash/7a685d9edd95508471a9d3d6fcace432-Abstract.html>PDF icon 2003.05562.pdf (2.51 MB)
Kim, S. & Spelke, E. S. Learning from multiple informants: Children’s response to epistemic bases for consensus judgments. Journal of Experimental Child Psychology 192, 104759 (2020).
Levine, S., Kleiman-Weiner, M., Schulz, L., Tenenbaum, J. B. & Cushman, F. A. The logic of universalization guides moral judgment. Proceedings of the National Academy of Sciences (PNAS) 202014505 (2020). doi:10.1073/pnas.2014505117
Poggio, T. & Cooper, Y. Loss landscape: SGD has a better view. (2020).PDF icon CBMM-Memo-107.pdf (1.03 MB)PDF icon Typos and small edits, ver11 (955.08 KB)PDF icon Small edits, corrected Hessian for spurious case (337.19 KB)
2021
Conwell, C. et al. Large-scale benchmarking of deep neural network models in mouse visual cortex reveals patterns similar to those observed in macaque visual cortex. Cosyne (2021).
Zaslavsky, N., Maldonado, M. & Culbertson, J. Let's talk (efficiently) about us: Person systems achieve near-optimal compression. Proceedings of the Annual Meeting of the Cognitive Science Society 43, (2021).
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)
Weisholtz, D. S. et al. Localized task-invariant emotional valence encoding revealed by intracranial recordingsAbstract. Social Cognitive and Affective Neuroscience (2021). doi:10.1093/scan/nsab134

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