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.

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Spokes, A. C. & Spelke, E. S. Early Reasoning about Affiliation and Social Networks. International Conference on Infant Studies (ICIS) (2016).

Roig, G., Chen, F., Boix, X. & Poggio, T. Eccentricity Dependent Deep Neural Networks for Modeling Human Vision. Vision Sciences Society (2017).

Chen, F., Roig, G., Isik, L., Boix, X. & Poggio, T. Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15360>

paper.pdf (963.87 KB)

Zhang, J., Han, Y., Poggio, T. & Roig, G. Eccentricity Dependent Neural Network with Recurrent Attention for Scale, Translation and Clutter Invariance . Vision Science Society (2019).

Młynarski, W. & McDermott, J. H. Ecological origins of perceptual grouping principles in the auditory system. Proceedings of the National Academy of Sciences 116, 25355 - 25364 (2019).

Azami, H. et al. EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer’s Disease. Journal of Alzheimer's Disease 91, 1557 - 1572 (2023).

N. Murty, A. Ratan & Arun, S. P. Effect of silhouetting and inversion on view invariance in the monkey inferotemporal cortex. Journal of Neurophysiology 11823, 353 - 362 (2017).

Dobs, K. et al. Effects of Face Familiarity in Humans and Deep Neural Networks . European Conference on Visual Perception (2019).

Kunhardt, O., Deza, A. & Poggio, T. The Effects of Image Distribution and Task on Adversarial Robustness. (2021).

CBMM_Memo_116.pdf (5.44 MB)

Sikarwar, A. & Kreiman, G. On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering. arXiv (2022). doi:10.48550/arXiv.2201.03965

On_the_Efficacy_of_Co-Attention_Transformer_Layers.pdf (35.54 MB)

Yildirim, I., Kulkarni, T., Freiwald, W. A. & Tenenbaum, J. B. Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations. Annual Conference of the Cognitive Science Society (2015).

yildirimetal_cogsci15.pdf (3.22 MB)

Yildirim, I., Freiwald, W. A. & J., T. Efficient inverse graphics in biological face processing. bioRxiv (2018). at <https://www.biorxiv.org/content/early/2018/04/02/282798>

Yildirim, I., Belledonne, M., Freiwald, W. A. & Tenenbaum, J. B. Efficient inverse graphics in biological face processing. Science Advances 6, eaax5979 (2020).

eaax5979.full_.pdf (3.22 MB)

Poggio, T. A. & Xu, M. On efficiently computable functions, deep networks and sparse compositionality. (2025).

Deep_sparse_networks_approximate_efficiently_computable_functions.pdf (223.15 KB)

Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Effort as a bridging concept across action and action understanding: Weight and Physical Effort in Predictions of Efficiency in Other Agents. International Conference on Infant Studies (ICIS) (2016).

Woo, B. & Spelke, E. Eight-Month-Old Infants’ Social Evaluations of Agents Who Act on False Beliefs. Proceedings of the Annual Meeting of the Cognitive Science Society 44, (2022).

Zaslavsky, N., Hu, J. & Levy, R. Emergence of Pragmatic Reasoning From Least-Effort Optimization . 13th International Conference on the Evolution of Language (EvoLang) (2020).

Bricken, T., Schaeffer, R., Olshausen, B. & Kreiman, G. Emergence of Sparse Representations from Noise. ICML 2023 (2023). at <https://openreview.net/pdf?id=cxYaBAXVKg>

Houlihan, S. Dae, Kleiman-Weiner, M., Hewitt, L. B., Tenenbaum, J. B. & Saxe, R. Emotion prediction as computation over a generative theory of mind. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 381, (2023).

houlihan2023computedappraisals.pdf (2.37 MB)

Lee, M. J. & DiCarlo, J. J. An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models. bioRxiv (2023). at <https://www.biorxiv.org/content/10.1101/2022.12.31.522402v1>

Kuo, Y. - L., Katz, B. & Barbu, A. Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. (2020).

CBMM-Memo-125.pdf (2.12 MB)

Kuo, Y. - L., Katz, B. & Barbu, A. Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020). doi:10.1109/IROS45743.2020.9341325

Griffiths, T. L. & Zaslavsky, N. Encyclopedia of Color Science and TechnologyBayesian Approaches to Color Category Learning. 1 - 5 (Springer Berlin Heidelberg, 2021). doi:10.1007/978-3-642-27851-8

Belbute-Peres, Fde Avila, Smith, K. A., Allen, K., Tenenbaum, J. B. & Kolter, Z. End-to-end differentiable physics for learning and control. Advances in Neural Information Processing Systems 31 (NIPS 2018) (2018).

7948-end-to-end-differentiable-physics-for-learning-and-control.pdf (794.17 KB)

Traer, J. & McDermott, J. H. Environmental statistics enable perceptual separation of sound and space. Speech and Audio in the Northeast (2016).

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