Publication

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2024
Poggio, T. & Fraser, M. Compositional sparsity of learnable functions. Bulletin of the American Mathematical Society 61, 438-456 (2024).
Poggio, T. & Fraser, M. Compositional Sparsity of Learnable Functions. (2024).PDF icon CBMM-Memo-145.pdf (1.25 MB)
Gan, Y. & Poggio, T. For HyperBFs AGOP is a greedy approximation to gradient descent. (2024).PDF icon CBMM-Memo-148.pdf (1.06 MB)
Gan, Y., Galanti, T., Poggio, T. & Malach, E. On the Power of Decision Trees in Auto-Regressive Language Modeling. (2024).PDF icon CBMM-Memo-149.pdf (2.11 MB)
2023
Poggio, T. & Magrini, M. Cervelli menti algoritmi. 272 (Sperling & Kupfer, 2023). at <https://www.sperling.it/libri/cervelli-menti-algoritmi-marco-magrini>
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)
Xu, M. et al. The Janus effects of SGD vs GD: high noise and low rank. (2023).PDF icon Updated with appendix showing empirically that the main results extend to deep nonlinear networks (2.95 MB)PDF icon Small updates...typos... (616.82 KB)
Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks. (2023).PDF icon Norm-based bounds for convnets.pdf (1.2 MB)
Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-based Generalization Bounds for Sparse Neural Networks. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/8493e190ff1bbe3837eca821190b61ff-Paper-Conference.pdf>PDF icon NeurIPS-2023-norm-based-generalization-bounds-for-sparse-neural-networks-Paper-Conference.pdf (577.69 KB)
Galanti, T., Siegel, Z., Gupte, A. & Poggio, T. SGD and Weight Decay Provably Induce a Low-Rank Bias in Deep Neural Networks. (2023).PDF icon Low-rank bias.pdf (2.38 MB)
2022
Gupte, A., Banburski, A. & Poggio, T. PCA as a defense against some adversaries. (2022).PDF icon CBMM-Memo-135.pdf (2.58 MB)
Anselmi, F. & Poggio, T. Representation Learning in Sensory Cortex: a theory. IEEE Access 1 - 1 (2022). doi:10.1109/ACCESS.2022.3208603PDF icon Representation_Learning_in_Sensory_Cortex_a_theory.pdf (1.17 MB)
Galanti, T. & Poggio, T. SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks. (2022).PDF icon Implicit Rank Minimization.pdf (1.76 MB)
Han, Y., Poggio, T. & Cheung, B. System identification of neural systems: If we got it right, would we know?. (2022).PDF icon CBMM-Memo-136.pdf (1.75 MB)
2021
Adler, A., Araya-Polo, M. & Poggio, T. Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows. IEEE Signal Processing Magazine 38, 89 - 119 (2021).
Banburski, A., De La Torre, F., Pant, N., Shastri, I. & Poggio, T. Distribution of Classification Margins: Are All Data Equal?. (2021).PDF icon CBMM Memo 115.pdf (9.56 MB)PDF icon arXiv version (23.05 MB)
Xu, M. et al. Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss. (2021).PDF icon v1.0 (4.61 MB)PDF icon v1.4corrections to generalization section (5.85 MB)PDF icon v1.7Small edits (22.65 MB)
Kunhardt, O., Deza, A. & Poggio, T. The Effects of Image Distribution and Task on Adversarial Robustness. (2021).PDF icon CBMM_Memo_116.pdf (5.44 MB)
Gant, J., Banburski, A., Deza, A. & Poggio, T. Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/Schedule?showEvent=21868>
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)
2020
Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121, 229 - 241 (2020).
Reddy, M. Vuyyuru, Banburski, A., Pant, N. & Poggio, T. Biologically Inspired Mechanisms for Adversarial Robustness. (2020).PDF icon CBMM_Memo_110.pdf (3.14 MB)
Poggio, T., Liao, Q. & Banburski, A. Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020).PDF icon s41467-020-14663-9.pdf (431.68 KB)
Malkin, E., Deza, A. & Poggio, T. CUDA-Optimized real-time rendering of a Foveated Visual System. Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020 (2020). at <https://arxiv.org/abs/2012.08655>PDF icon Foveated_Drone_SVRHM_2020.pdf (13.44 MB)PDF icon v1 (12/15/2020) (14.7 MB)

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