Publication

Found 904 results
[ Author(Desc)] Title Type Year
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 
U
Ullman, S., Dorfman, N. & Harari, D. A model for discovering ‘containment’ relations. Cognition 183, 67 - 81 (2019).
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).
Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Critical Cues in Early Physical Reasoning. SRCD (2017).
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)
Ullman, S. et al. Image interpretation by iterative bottom-up top- down processing. (2021).PDF icon CBMM-Memo-120.pdf (2.83 MB)
Ullman, T. D. & Tenenbaum, J. B. Bayesian Models of Conceptual Development: Learning as Building Models of the World. Annual Review of Developmental Psychology 2, 533 - 558 (2020).
Ullman, T. D., Spelke, E. S., Battaglia, P. & Tenenbaum, J. B. Mind Games: Game Engines as an Architecture for Intuitive Physics. Trends in Cognitive Science 21, 649 - 665 (2017).PDF icon Preprint submitted to Trends in Cognitive Science (17.64 MB)
Ullman, T. D. et al. Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects. Cognitive Science Society (2019). at <https://mindmodeling.org/cogsci2019/papers/0506/index.html>PDF icon Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects.pdf (2.62 MB)
Ullman, S. Using neuroscience to develop artificial intelligence. Science 363, 692 - 693 (2019).
V
Valente, S., Marques, T. & Lima, S. Q. No evidence for prolactin’s involvement in the post-ejaculatory refractory periodAbstract. Communications Biology 4, (2021).
Varela, C. & Wilson, M. A. Thalamic contribution to CA1-mPFC interactions during sleep. Society for Neuroscience's Annual Meeting - SfN 2017 (2017).File AbstractSFNfinal.docx (13.14 KB)
Vaziri-Pashkam, M. Predicting actions before they occur. (2015).PDF icon PredictingActions (1.43 MB)File Supplemental Video 1: Experimental set up and task (16.38 MB)File Supplemental Video 2: An example FullVid and CutVid trial clips from experiment 4 (5.47 MB)
Vaziri-Pashkam, M., Cormiea, S. & Nakayama, K. Predicting actions from subtle preparatory movements. Cognition 168, 65 - 75 (2017).
Vazquez, Y., Ianni, G. & Freiwald, W. A. Neural mechanisms supporting facial expressions . unknown (2019).
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
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).
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).
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_7PDF icon Author's Version (147.25 KB)
Villalobos, K. M. et al. Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018).PDF icon CBMM-Memo-095.pdf (1.96 MB)
Villalobos, K. M. et al. Do Neural Networks for Segmentation Understand Insideness?. (2020).PDF icon CBMM-Memo-105.pdf (4.63 MB)PDF icon CBMM Memo 105 v2 (July 2, 2020) (3.2 MB)PDF icon CBMM Memo 105 v3 (January 25, 2022) (8.33 MB)
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).PDF icon gk7967.pdf (3.07 MB)
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>
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Speech Representations based on a Theory for Learning Invariances. (2014).
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).PDF icon CBMM-Memo-069.pdf (6.47 MB)

Pages