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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W

Wang, B., Mayo, D., Deza, A., Barbu, A. & Conwell, C. On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation. Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS (2021). at <https://openreview.net/forum?id=Rpazl253IHb>

Wang, C., Wang, Y., Lin, Z., Yuille, A. & Gao, W. Robust Estimation of 3D Human Poses from a Single Image. (2014).

CBMM-Memo-013.pdf (510.23 KB)

Wang, Y. - S., Liu, C., Zeng, X. & Yuille, A. Scene Graph Parsing as Dependency Parsing. (2018).

CBMM-Memo-082.pdf (869 KB)

Wang, J. et al. Detecting Semantic Parts on Partially Occluded Objects. (2017).

CBMM-Memo-078.pdf (1.74 MB)

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Volokitin, A. & Roig, G. Do Deep Neural Networks Suffer from Crowding? [code]. (2017).

Volokitin, A., Roig, G. & Poggio, T. Do Deep Neural Networks Suffer from Crowding?. (2017).

CBMM-Memo-069.pdf (6.47 MB)

Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Speech Representations based on a Theory for Learning Invariances. (2014).

Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Word-level Invariant Representations From Acoustic Waveforms. 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_2385.html>

Vinken, K., Boix, X. & Kreiman, G. Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception. Science Advances 6, eabd4205 (2020).

gk7967.pdf (3.07 MB)

Villalobos, K. M. et al. Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018).

CBMM-Memo-095.pdf (1.96 MB)

Villalobos, K. M. et al. Do Neural Networks for Segmentation Understand Insideness?. (2020).

CBMM-Memo-105.pdf (4.63 MB)

CBMM Memo 105 v2 (July 2, 2020) (3.2 MB)

CBMM Memo 105 v3 (January 25, 2022) (8.33 MB)

Villa, S. et al. Empirical Inference 59 - 69 (Springer Berlin Heidelberg, 2013). doi:10.1007/978-3-642-41136-610.1007/978-3-642-41136-6_7

Author's Version (147.25 KB)

Villa, S., Matet, S., Vũ, B. Công & Rosasco, L. Implicit regularization with strongly convex bias: Stability and acceleration. Analysis and Applications 21, 165 - 191 (2023).

Vélez, N., Chen, A. M., Burke, T., Cushman, F. A. & Gershman, S. J. Teachers recruit mentalizing regions to represent learners’ beliefs. Proceedings of the National Academy of Sciences 120, (2023).

Vega, C., Molinari, C., Rosasco, L. & Villa, S. Fast iterative regularization by reusing dataAbstract. Journal of Inverse and Ill-posed Problems (2023). doi:10.1515/jiip-2023-0009

Vazquez, Y., Ianni, G. & Freiwald, W. A. Neural mechanisms supporting facial expressions . unknown (2019).

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)

Varela, C. & Wilson, M. A. Thalamic contribution to CA1-mPFC interactions during sleep. Society for Neuroscience's Annual Meeting - SfN 2017 (2017).

AbstractSFNfinal.docx (13.14 KB)

Valente, S., Marques, T. & Lima, S. Q. No evidence for prolactin’s involvement in the post-ejaculatory refractory periodAbstract. Communications Biology 4, (2021).

U

Ullman, S., Dorfman, N. & Harari, D. A model for discovering ‘containment’ relations. Cognition 183, 67 - 81 (2019).

Ullman, S., Assif, L., Fetaya, E. & Harari, D. Atoms of recognition in human and computer vision. PNAS 113, 2744–2749 (2016).

mirc_author_manuscript_with_figures_and_SI-2.pdf (1.65 MB)

Ullman, T. D., Stuhlmüller, A., Goodman, N. D. & Tenenbaum, J. B. Learning physical parameters from dynamic scenes. Cognitive Psychology 104, 57-82 (2018).

T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)

Ullman, S. et al. Image interpretation by iterative bottom-up top- down processing. (2021).

CBMM-Memo-120.pdf (2.83 MB)

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).

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