Publication
] Two areas for familiar face recognition in the primate brain. Science 357, 591 - 595 (2017).
591.full_.pdf (928.29 KB)
591.full_.pdf (928.29 KB) Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Magazine 37 , 73-77 (2016). Turing_Plus_Questions.pdf (424.91 KB)
Turing_Plus_Questions.pdf (424.91 KB) Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. 34th International Conference on Machine Learning 70, 1733-1741 (2017). 1612.05231.pdf (2.3 MB)
1612.05231.pdf (2.3 MB) The Trolley Problem [Edge.com]. (2016). at <https://www.edge.org/response-detail/27051> The Trolley Problem.pdf (343.3 KB)
The Trolley Problem.pdf (343.3 KB) Transformer Module Networks for Systematic Generalization in Visual Question Answering. (2022). CBMM-Memo-121.pdf (1.06 MB) version 2 (3/22/2023) (1.33 MB)
CBMM-Memo-121.pdf (1.06 MB) version 2 (3/22/2023) (1.33 MB) Trajectory Prediction with Linguistic Representations. 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022). doi:10.1109/ICRA46639.2022.9811928
Trajectory Prediction with Linguistic Representations. (2022). CBMM-Memo-132.pdf (1.15 MB)
CBMM-Memo-132.pdf (1.15 MB) Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images. NIPS 2016 (2016). 6590-training-and-evaluating-multimodal-word-embeddings-with-large-scale-web-annotated-images.pdf (1.57 MB)
6590-training-and-evaluating-multimodal-word-embeddings-with-large-scale-web-annotated-images.pdf (1.57 MB) Trading robust representations for sample complexity through self-supervised visual experience. Advances in Neural Information Processing Systems 31 () 9640–9650 (Curran Associates, Inc., 2018). at <http://papers.nips.cc/paper/8170-trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf> trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB) NeurIPS2018_Poster.pdf (6.12 MB)
trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB) NeurIPS2018_Poster.pdf (6.12 MB) 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>
Towards a Programmer's Apprentice (Again). (2015). CBMM-memo-030.pdf (294.27 KB)
CBMM-memo-030.pdf (294.27 KB) Toward human-like object naming in artificial neural systems . International Conference on Learning Representations (ICLR 2020), Bridging AI and Cognitive Science workshop (2020).
Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods. Image and Vision Computing 142, 104894 (2024).
The History of Neuroscience in Autobiography Volume 8 8, (Society for Neuroscience, 2014). Volume Introduction and Preface (232.8 KB) TomasoPoggio.pdf (1.43 MB)
Volume Introduction and Preface (232.8 KB) TomasoPoggio.pdf (1.43 MB) To What Extent Does Global Shape Influence Category Representation in the Brain?. Journal of Neuroscience 36, 4149 - 4151 (2016).
To find better neural network models of human vision, find better neural network models of primate vision. BioRxiv (2019). at <https://www.biorxiv.org/content/10.1101/688390v1.full>
Timing, timing, timing: Fast decoding of object inforrmation from intracranial field potentials in human visual cortex. (2009). at <http://klab.tch.harvard.edu/resources/liuetal_timing3.html>
Time-dependent discrimination advantages for harmonic sounds suggest efficient coding for memory. Proceedings of the National Academy of Sciences 117, 32169 - 32180 (2020).
ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation. (2020). at <http://www.threedworld.org/>
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv (2020). at <https://arxiv.org/abs/2007.04954> 2007.04954.pdf (7.06 MB)
2007.04954.pdf (7.06 MB)