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Tacchetti, A., Voinea, S., Evangelopoulos, G. & Poggio, T. Representation Learning from Orbit Sets for One-shot Classification. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15357>
Tacchetti, A., Voinea, S. & Evangelopoulos, G. Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets. (2017).PDF icon CBMM-Memo-062.pdf (9.37 MB)
Tacchetti, A., Isik, L. & Poggio, T. Invariant representations for action recognition in the visual system. Vision Sciences Society 15, (2015).
Tacchetti, A., Isik, L. & Poggio, T. Invariant action recognition dataset. (2017). at <https://doi.org/10.7910/DVN/DMT0PG>
Tacchetti, A., Isik, L. & Poggio, T. Invariant Recognition Shapes Neural Representations of Visual Input. Annual Review of Vision Science 4, 403 - 422 (2018).PDF icon annurev-vision-091517-034103.pdf (1.55 MB)
Tacchetti, A., Isik, L. & Poggio, T. Invariant recognition drives neural representations of action sequences. PLoS Comp. Bio (2017).
Tacchetti, A., Isik, L. & Poggio, T. Spatio-temporal convolutional networks explain neural representations of human actions. (2016).
Tan, C. & Poggio, T. Neural tuning size is a key factor underlying holistic face processing. (2014).PDF icon CBMM-Memo-021-1406.3793.pdf (387.79 KB)
Tan, C. & Poggio, T. Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing. Public Library of Science | PLoS ONE 1(3): e0150980, (2016).PDF icon journal.pone_.0150980.PDF (384.15 KB)
Tang, H., Buia, C., Madsen, J., Anderson, W. S. & Kreiman, G. A role for recurrent processing in object completion: neurophysiological, psychophysical and computational evidence. (2014).PDF icon CBMM-Memo-009.pdf (4.21 MB)
Tang, H. et al. A machine learning approach to predict episodic memory formation. 2016 Annual Conference on Information Science and Systems (CISS) 539 - 544 (2016). doi:10.1109/CISS.2016.7460560
Tang, H., Kreiman, G. & Zhao, Q. Computational and Cognitive Neuroscience of Vision (Springer Singapore, 2017). at <http://www.springer.com/us/book/9789811002113>
Tang, H. et al. Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex. Neuron 83, 736 - 748 (2014).
Tang, H. et al. Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115PDF icon 1719397115.full_.pdf (1.1 MB)
Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352PDF icon Manuscript  (1.83 MB)
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
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Tazi, Y., Berger, M. & Freiwald, W. A. Towards an objective characterization of an individual's facial movements using Self-Supervised Person-Specific-Models. arXiv (2022). at <https://arxiv.org/abs/2211.08279>
Tegmark, M. Improved Measures of Integrated Information. PLOS Computational Biology (2016). doi:10.1371/journal.pcbi.100512310.1371PDF icon 1601.02626.pdf (3.49 MB)
Tejwani, R., Kuo, Y. - L., Shu, T., Katz, B. & Barbu, A. Social Interactions as Recursive MDPs. (2021).PDF icon CBMM-Memo-130.pdf (1.52 MB)
Tejwani, R. et al. Zero-shot linear combinations of grounded social interactions with Linear Social MDPs. Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI) (2023).
Tejwani, R. et al. Incorporating Rich Social Interactions Into MDPs. (2022).PDF icon CBMM-Memo-133.pdf (1.68 MB)

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