Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

Videos
Support Us

Publication

Search

Show only items where

Author

Type

Term

Year

Keyword

Found 912 results

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

P

Yan, P., Magid, R. & Schulz, L. Preschoolers expect others to learn rationally from evidence. Annual Conference of the Cognitive Science Society (2014).

Yan, Magid, & Schulz_CogSci14_REVISED.pdf (302.4 KB)

Liu, S., Brooks, N. B. & Spelke, E. S. Pre-reaching infants expect causal agents to act efficiently without motor training. 20th Biennial International Conference on Infant Studies (ICIS) (2016).

Kosakowski, H. L. et al. Preliminary evidence for selective cortical responses to music in one‐month‐old infants. Developmental Science (2023). doi:10.1111/desc.13387

Developmental Science - 2023 - Kosakowski - Preliminary evidence for selective cortical responses to music in one‐month‐old.pdf (2.6 MB)

Lotter, W., Kreiman, G. & Cox, D. PredNet - "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning" [code]. (2016).

Xu, J., Jiang, M., Wang, S., Kankanhalli, M. & Zhao, Q. Predicting Saliency Beyond Pixels. (2014). at <http://www.ece.nus.edu.sg/stfpage/eleqiz/predicting.html>

Berzak, Y., Nakamura, C., Flynn, S. & Katz, B. Predicting Native Language from Gaze. Annual Meeting of the Association for Computational Linguistics (ACL 2017) (2017).

Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).

Tang, H. et al. Predicting episodic memory formation for movie events [code]. (2016).

Tang, H. et al. Predicting episodic memory formation for movie events. Scientific Reports (2016). doi:10.1038/srep30175

Vaziri-Pashkam, M., Cormiea, S. & Nakayama, K. Predicting actions from subtle preparatory movements. Cognition 168, 65 - 75 (2017).

Vaziri-Pashkam, M. Predicting actions before they occur. (2015).

PredictingActions (1.43 MB)

Supplemental Video 1: Experimental set up and task (16.38 MB)

Supplemental Video 2: An example FullVid and CutVid trial clips from experiment 4 (5.47 MB)

Bergen, L., Levy, R. & Goodman, N. D. Pragmatic Reasoning through Semantic Inference. Semantics and Pragmatics Vol 9 (2016) , (2016).

BergenLevyGoodman2015.pdf (1.12 MB)

Gan, Y., Galanti, T., Poggio, T. & Malach, E. On the Power of Decision Trees in Auto-Regressive Language Modeling. (2024).

CBMM-Memo-149.pdf (2.11 MB)

Danhofer, D. A., D’Ascenzo, D., Dubach, R. & Poggio, T. Position: A Theory of Deep Learning Must Include Compositional Sparsity. (2025).

CBMM Memo 159.pdf (676.35 KB)

Buice, M. & de Vries, S. Population Coding, Correlations, and Functional Connectivity in the mouse visual system with the Cortical Activity Map (CAM). Society for Neuroscience 2015 (2015).

2015 SFN Population_Coding.pdf (2.94 MB)

Kryven, M., Ullman, T. D., Cowan, W. & Tenenbaum, J. B. Plans or Outcomes: How Do We Attribute Intelligence to Others?. Cognitive Science 45, (2021).

Kool, W., Gershman, S. J. & Cushman, F. A. Planning Complexity Registers as a Cost in Metacontrol. Journal of Cognitive Neuroscience 30, 1391 - 1404 (2018).

Kulkarni, T., Kohli, P., Tenenbaum, J. B. & Mansinghka, V. Picture: An Imperative Probabilistic Programming Language for Scene Perception. Computer Vision and Pattern Recognition (2015).

Lagomarsino-Oneto, D. et al. Physics informed machine learning for wind speed prediction. Energy 268, 126628 (2023).

Yildirim, I., Gerstenberg, T., Saeed, B., Toussant, M. & Tenenbaum, J. B. Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints. Proceedings of the 39th Annual Conference of the Cognitive Science Society (2017).

Physical problem solving Joint planning with symbolic, geometric, and dynamic constraints, Yildirim et al., 2017.pdf (2.46 MB)

Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L. & Poggio, T. Phone Classification by a Hierarchy of Invariant Representation Layers. INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), 2014). at <http://www.isca-speech.org/archive/interspeech_2014/i14_2346.html>

Netanyahu, A., Shu, T., Katz, B., Barbu, A. & Tenenbaum, J. B. PHASE: PHysically-grounded Abstract Social Eventsfor Machine Social Perception. Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020 (2020). at <https://openreview.net/forum?id=_bokm801zhx>

phase_physically_grounded_abstract_social_events_for_machine_social_perception.pdf (2.49 MB)

Netanyahu, A., Shu, T., Katz, B., Barbu, A. & Tenenbaum, J. B. PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception. (2021).

CBMM-Memo-123.pdf (3.08 MB)

Netanyahu, A., Shu, T., Katz, B., Barbu, A. & Tenenbaum, J. B. PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception. AAAI-21 (2021).

Poggio, T. A Perspective: Sparse Compositionality and Efficiently Computable Intelligence. (2026).

Perspective_SPCOMP-9.pdf (170.23 KB)

Pages