Publication
Found 13 results
Author Title Type [ Year
] Filters: Author is Tomaso Poggio and First Letter Of Title is F [Clear All Filters]
For HyperBFs AGOP is a greedy approximation to gradient descent. (2024).
CBMM-Memo-148.pdf (1.06 MB)
Formation of Representations in Neural Networks. (2024).
CBMM-Memo-150.pdf (4.03 MB)
Feature learning in deep classifiers through Intermediate Neural Collapse. (2023).
Feature_Learning_memo.pdf (2.16 MB)
For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability. Analysis and Applications 21, 193 - 215 (2023).
From Associative Memories to Powerful Machines. (2021).
v1.0 (1.01 MB)
v1.3Section added August 6 on self attention (3.9 MB)
From Marr’s Vision to the Problem of Human Intelligence. (2021).
CBMM-Memo-118.pdf (362.19 KB)
For interpolating kernel machines, the minimum norm ERM solution is the most stable. (2020).
CBMM_Memo_108.pdf (1015.14 KB)
Better bound (without inequalities!) (1.03 MB)
Function approximation by deep networks. Communications on Pure & Applied Analysis 19, 4085 - 4095 (2020).
1534-0392_2020_8_4085.pdf (514.57 KB)
A fast, invariant representation for human action in the visual system. Journal of Neurophysiology (2018). doi:https://doi.org/10.1152/jn.00642.2017
A fast, invariant representation for human action in the visual system. J Neurophysiol jn.00642.2017 (2017). doi:10.1152/jn.00642.2017
Author's last draft (695.63 KB)
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. arXiv.org (2017). at <https://arxiv.org/abs/1711.01530>
1711.01530.pdf (966.99 KB)
Fast, invariant representation for human action in the visual system. (2016). at <http://arxiv.org/abs/1601.01358>
CBMM Memo 042 (3.03 MB)
Foveation-based Mechanisms Alleviate Adversarial Examples. (2016).
cbmm_memo_044.pdf (11.48 MB)