Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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Found 159 results

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Filters: Author is Tomaso A. Poggio [Clear All Filters]

2024

Gan, Y. & Poggio, T. For HyperBFs AGOP is a greedy approximation to gradient descent. (2024).

CBMM-Memo-148.pdf (1.06 MB)

Ziyin, L., Chuang, I., Galanti, T. & Poggio, T. Formation of Representations in Neural Networks. (2024).

CBMM-Memo-150.pdf (4.03 MB)

Pinto, A., Rangamani, A. & Poggio, T. On Generalization Bounds for Neural Networks with Low Rank Layers. (2024).

CBMM-Memo-151.pdf (697.31 KB)

Gan, Y., Galanti, T., Poggio, T. & Malach, E. On the Power of Decision Trees in Auto-Regressive Language Modeling. (2024).

CBMM-Memo-149.pdf (2.11 MB)

Liao, Q. et al. Self-Assembly of a Biologically Plausible Learning Circuit. (2024).

CBMM-Memo-152.pdf (1.84 MB)

2025

Poggio, T. A. & Xu, M. On efficiently computable functions, deep networks and sparse compositionality. (2025).

Deep_sparse_networks_approximate_efficiently_computable_functions.pdf (223.15 KB)

Dubach, R., Abdallah, M. S. & Poggio, T. Multiplicative Regularization Generalizes Better Than Additive Regularization. (2025).

CBMM Memo 158.pdf (4.8 MB)

Danhofer, D. A., D’Ascenzo, D., Dubach, R. & Poggio, T. Position: A Theory of Deep Learning Must Include Compositional Sparsity. (2025).

CBMM Memo 159.pdf (676.35 KB)

Tiwary, K. et al. What if Eye..? Computationally Recreating Vision Evolution. arXiv (2025). at <https://arxiv.org/abs/2501.15001>

2501.15001v1.pdf (5.2 MB)

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