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Compositional Sparsity of Learnable Functions. (2024).
CBMM-Memo-145.pdf (1.25 MB)
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Cervelli menti algoritmi. 272 (Sperling & Kupfer, 2023). at <https://www.sperling.it/libri/cervelli-menti-algoritmi-marco-magrini>
Feature learning in deep classifiers through Intermediate Neural Collapse. (2023).
Feature_Learning_memo.pdf (2.16 MB)
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A Homogeneous Transformer Architecture. (2023).
CBMM-Memo-143.pdf (1.07 MB)
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The Janus effects of SGD vs GD: high noise and low rank. (2023).
Updated with appendix showing empirically that the main results extend to deep nonlinear networks (2.95 MB)
Small updates...typos... (616.82 KB)
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Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks. (2023).
Norm-based bounds for convnets.pdf (1.2 MB)
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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>
NeurIPS-2023-norm-based-generalization-bounds-for-sparse-neural-networks-Paper-Conference.pdf (577.69 KB)
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SGD and Weight Decay Provably Induce a Low-Rank Bias in Deep Neural Networks. (2023).
Low-rank bias.pdf (2.38 MB)
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System Identification of Neural Systems: If We Got It Right, Would We Know?. Proceedings of the 40th International Conference on Machine Learning, PMLR 202, 12430-12444 (2023).
han23d.pdf (797.48 KB)
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How Deep Sparse Networks Avoid the Curse of Dimensionality: Efficiently Computable Functions are Compositionally Sparse. (2022).
v1.0 (984.15 KB)
v5.7 adding in context learning etc (1.16 MB)
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PCA as a defense against some adversaries. (2022).
CBMM-Memo-135.pdf (2.58 MB)
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Representation Learning in Sensory Cortex: a theory. IEEE Access 1 - 1 (2022). doi:10.1109/ACCESS.2022.3208603
Representation_Learning_in_Sensory_Cortex_a_theory.pdf (1.17 MB)
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Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows. IEEE Signal Processing Magazine 38, 89 - 119 (2021).
Distribution of Classification Margins: Are All Data Equal?. (2021).
CBMM Memo 115.pdf (9.56 MB)
arXiv version (23.05 MB)
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Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss. (2021).
v1.0 (4.61 MB)
v1.4corrections to generalization section (5.85 MB)
v1.7Small edits (22.65 MB)
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The Effects of Image Distribution and Task on Adversarial Robustness. (2021).
CBMM_Memo_116.pdf (5.44 MB)
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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>
From Associative Memories to Powerful Machines. (2021).
v1.0 (1.01 MB)
v1.3Section added August 6 on self attention (3.9 MB)
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From Marr’s Vision to the Problem of Human Intelligence. (2021).
CBMM-Memo-118.pdf (362.19 KB)
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An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121, 229 - 241 (2020).
Biologically Inspired Mechanisms for Adversarial Robustness. (2020).
CBMM_Memo_110.pdf (3.14 MB)
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Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020).
s41467-020-14663-9.pdf (431.68 KB)
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