Publication

Found 49 results
Author Title Type [ Year(Desc)]
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2018
Ben-Yosef, G., Assif, L. & Ullman, S. Full interpretation of minimal images. Cognition 171, 65-84 (2018).PDF icon Full interpretation of minimal images.pdf (4.55 MB)
Ben-Yosef, G., Assif, L. & Ullman, S. Full interpretation of minimal images. Cognition 171, 65 - 84 (2018).
2019
Obiajulu, D., Vazquez, Y., Ianni, G. A., Yazdani, F. & Freiwald, W. A. Facial Expression Scoring and Assessment of Facial Movement Kinematics in Non-Human Primates. The Rockefeller University 2019 Summer Science Research Program (SSRP) (2019).
Araya-Polo, M., Adler, A., Farris, S. & Jennings, J. Deep Learning: Algorithms and Applications (SPRINGER-VERLAG, 2019).
Serrino, J., Kleiman-Weiner, M., Parkes, D. C. & Tenenbaum, J. B. Finding Friend and Foe in Multi-Agent Games. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon Max KW paper.pdf (928.96 KB)
2020
Schaeffer, D. J. et al. Face selective patches in marmoset frontal cortexAbstract. Nature Communications 11, (2020).
Kar, K. & DiCarlo, J. J. Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition. Neuron (2020). doi:10.1016/j.neuron.2020.09.035PDF icon PIIS0896627320307595.pdf (3.92 MB)
Smith, K. A. et al. The fine structure of surprise in intuitive physics: when, why, and how much?. Proceedings of the 42th Annual Meeting of the Cognitive Science Society - Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29 - August 1, 2020 (Denison, S., Mack, M., Xu, Y. & Armstrong, B. C.) (2020). at <https://cogsci.mindmodeling.org/2020/papers/0761/index.html>
Rangamani, A., Rosasco, L. & Poggio, T. For interpolating kernel machines, the minimum norm ERM solution is the most stable. (2020).PDF icon CBMM_Memo_108.pdf (1015.14 KB)PDF icon Better bound (without inequalities!) (1.03 MB)
Mhaskar, H. & Poggio, T. Function approximation by deep networks. Communications on Pure & Applied Analysis 19, 4085 - 4095 (2020).PDF icon 1534-0392_2020_8_4085.pdf (514.57 KB)
2021
Landi, S. M., Viswanathan, P., Serene, S. & Freiwald, W. A. A fast link between face perception and memory in the temporal pole. Science eabi6671 (2021). doi:10.1126/science.abi6671
Kar, K. & DiCarlo, J. J. Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition. Neuron 109, 164 - 176.e5 (2021).
McNamee, D., Stachenfeld, K., Botvinick, M. M. & Gershman, S. J. Flexible modulation of sequence generation in the entorhinal-hippocampal system. Nature Neuroscience (2021). doi:10.1038/s41593-021-00831-7
Casper, S. et al. Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021 (2021). at <https://dblp.org/rec/conf/aaai/CasperBDGSVK21.html>PDF icon 1912.04783.pdf (6.69 MB)
Poggio, T. From Associative Memories to Powerful Machines. (2021).PDF icon v1.0 (1.01 MB)PDF icon v1.3Section added August 6 on self attention (3.9 MB)
Poggio, T. From Marr’s Vision to the Problem of Human Intelligence. (2021).PDF icon CBMM-Memo-118.pdf (362.19 KB)
2022
Bardon, A., Xiao, W., Ponce, C. R., Livingstone, M. S. & Kreiman, G. Face neurons encode nonsemantic features. Proceedings of the National Academy of Sciences 119, (2022).
Harrington, A. & Deza, A. Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks. International Conference on Learning Representations (ICLR) (2022). at <https://openreview.net/forum?id=yeP_zx9vqNm>
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
Rangamani, A., Lindegaard, M., Galanti, T. & Poggio, T. Feature learning in deep classifiers through Intermediate Neural Collapse. (2023).PDF icon Feature_Learning_memo.pdf (2.16 MB)
Rangamani, A., Rosasco, L. & Poggio, T. For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability. Analysis and Applications 21, 193 - 215 (2023).
Srinivasan, R. Francesco et al. Forward learning with top-down feedback: empirical and analytical characterization. arXiv (2023). at <https://arxiv.org/abs/2302.05440>
2024
Gan, Y. & Poggio, T. For HyperBFs AGOP is a greedy approximation to gradient descent. (2024).PDF icon CBMM-Memo-148.pdf (1.06 MB)
Ziyin, L., Chuang, I., Galanti, T. & Poggio, T. Formation of Representations in Neural Networks. (2024).PDF icon CBMM-Memo-150.pdf (4.03 MB)

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