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2202.00565
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Data-driven emergence of convolutional structure in neural networks
Proceedings of the National Academy of Sciences of the United States of America (PNAS), 2022
1 February 2022
Alessandro Ingrosso
Sebastian Goldt
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Papers citing
"Data-driven emergence of convolutional structure in neural networks"
19 / 19 papers shown
Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions
Fabiola Ricci
Lorenzo Bardone
Sebastian Goldt
OOD
497
5
0
31 Mar 2025
Nonlinear dynamics of localization in neural receptive fields
Neural Information Processing Systems (NeurIPS), 2025
Leon Lufkin
Andrew M. Saxe
Erin Grant
341
2
0
28 Jan 2025
Classifying Overlapping Gaussian Mixtures in High Dimensions: From Optimal Classifiers to Neural Nets
Khen Cohen
Noam Levi
Yaron Oz
BDL
270
1
0
28 May 2024
Sliding down the stairs: how correlated latent variables accelerate learning with neural networks
Lorenzo Bardone
Sebastian Goldt
329
13
0
12 Apr 2024
Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks
Eszter Székely
Lorenzo Bardone
Federica Gerace
Sebastian Goldt
436
3
0
22 Dec 2023
Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks
Nature Communications (Nat. Commun.), 2023
R. Aiudi
R. Pacelli
A. Vezzani
R. Burioni
P. Rotondo
MLT
319
29
0
21 Jul 2023
Loss Dynamics of Temporal Difference Reinforcement Learning
Neural Information Processing Systems (NeurIPS), 2023
Blake Bordelon
P. Masset
Henry Kuo
Cengiz Pehlevan
AI4CE
323
0
0
10 Jul 2023
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
Physical Review X (PRX), 2023
Francesco Cagnetta
Leonardo Petrini
Umberto M. Tomasini
Alessandro Favero
Matthieu Wyart
BDL
605
61
0
05 Jul 2023
Mapping of attention mechanisms to a generalized Potts model
Physical Review Research (Phys. Rev. Res.), 2023
Riccardo Rende
Federica Gerace
Alessandro Laio
Sebastian Goldt
479
40
0
14 Apr 2023
Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalisation
Simone Ciceri
Lorenzo Cassani
Matteo Osella
P. Rotondo
P. Pizzochero
M. Gherardi
369
8
0
09 Mar 2023
Optimal transfer protocol by incremental layer defrosting
Federica Gerace
Diego Doimo
Stefano Sarao Mannelli
Luca Saglietti
Alessandro Laio
CLL
203
3
0
02 Mar 2023
Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation
Luca Pesce
Florent Krzakala
Bruno Loureiro
Ludovic Stephan
291
32
0
17 Feb 2023
Neural networks trained with SGD learn distributions of increasing complexity
International Conference on Machine Learning (ICML), 2022
Maria Refinetti
Alessandro Ingrosso
Sebastian Goldt
UQCV
406
58
0
21 Nov 2022
A simple probabilistic neural network for machine understanding
Journal of Statistical Mechanics: Theory and Experiment (JSTAT), 2022
Rongrong Xie
M. Marsili
OCL
AI4CE
286
4
0
24 Oct 2022
What Can Be Learnt With Wide Convolutional Neural Networks?
International Conference on Machine Learning (ICML), 2022
Francesco Cagnetta
Alessandro Favero
Matthieu Wyart
MLT
674
16
0
01 Aug 2022
Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm
International Conference on Machine Learning (ICML), 2022
Lechao Xiao
Jeffrey Pennington
277
12
0
11 Jul 2022
Learning sparse features can lead to overfitting in neural networks
Neural Information Processing Systems (NeurIPS), 2022
Leonardo Petrini
Francesco Cagnetta
Eric Vanden-Eijnden
Matthieu Wyart
MLT
344
40
0
24 Jun 2022
The impact of memory on learning sequence-to-sequence tasks
Alireza Seif
S. Loos
Gennaro Tucci
É. Roldán
Sebastian Goldt
249
6
0
29 May 2022
Gaussian Universality of Perceptrons with Random Labels
Physical Review E (Phys. Rev. E), 2022
Federica Gerace
Florent Krzakala
Bruno Loureiro
Ludovic Stephan
Lenka Zdeborová
365
30
0
26 May 2022
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