Open Access Helper

OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Data-driven emergence of convolutional structure in neural networks
Alessandro Ingrosso, Sebastian Goldt
Proceedings of the National Academy of Sciences (2022) Vol. 119, Iss. 40
Open Access | Times Cited: 22

Showing 22 citing articles:

A simple linear algebra identity to optimize large-scale neural network quantum states
Riccardo Rende, Luciano Loris Viteritti, Lorenzo Bardone, et al.
Communications Physics (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 21
Gaussian universality of perceptrons with random labels
Federica Gerace, Florent Krząkała, Bruno Loureiro, et al.
Physical review. E (2024) Vol. 109, Iss. 3
Open Access | Times Cited: 11
Mapping of attention mechanisms to a generalized Potts model
Riccardo Rende, Federica Gerace, Alessandro Laio, et al.
Physical Review Research (2024) Vol. 6, Iss. 2
Open Access | Times Cited: 11
Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks
Riccardo Aiudi, R. Pacelli, Piero Baglioni, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access | Times Cited: 1
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
Francesco Cagnetta, Leonardo Petrini, Umberto Maria Tomasini, et al.
Physical Review X (2024) Vol. 14, Iss. 3
Open Access | Times Cited: 7
Inversion dynamics of class manifolds in deep learning reveals tradeoffs underlying generalization
Simone Ciceri, Lucía Cassani, Matteo Osella, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 1, pp. 40-47
Closed Access | Times Cited: 4
Prediction of Member Forces of Steel Tubes on the Basis of a Sensor System with the Use of AI
Haiyu Li, Heung‐Jin Chung
Sensors (2025) Vol. 25, Iss. 3, pp. 919-919
Open Access
Neural networks trained with SGD learn distributions of increasing complexity*
Maria Refinetti, Alessandro Ingrosso, Sebastian Goldt
Journal of Statistical Mechanics Theory and Experiment (2025) Vol. 2025, Iss. 2, pp. 024001-024001
Open Access
Observing Schrödinger’s cat with artificial intelligence: emergent classicality from information bottleneck
Zhelun Zhang, Yi‐Zhuang You
Machine Learning Science and Technology (2024) Vol. 5, Iss. 1, pp. 015051-015051
Open Access | Times Cited: 3
Comparison of the Capacity of Several Machine Learning Tools to Assist Immunofluorescence-Based Detection of Anti-Neutrophil Cytoplasmic Antibodies
Daniel Bertin, Pierre Bongrand, Nathalie Bardin
International Journal of Molecular Sciences (2024) Vol. 25, Iss. 6, pp. 3270-3270
Open Access | Times Cited: 3
Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure–Activity Relationships
Yasunari Matsuzaka, Yoshihiro Uesawa
Molecules (2023) Vol. 28, Iss. 5, pp. 2410-2410
Open Access | Times Cited: 6
What does self-attention learn from Masked Language Modelling?
Riccardo Rende, Federica Gerace, Alessandro Laio, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 4
A simple probabilistic neural network for machine understanding
Rongrong Xie, Matteo Marsili
Journal of Statistical Mechanics Theory and Experiment (2024) Vol. 2024, Iss. 2, pp. 023403-023403
Open Access | Times Cited: 1
Convolutional architectures are cortex-aligned de novo
Atlas Kazemian, Eric Elmoznino, Michael Bonner
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
What can be learnt with wide convolutional neural networks?*
Francesco Cagnetta, Alessandro Favero, Matthieu Wyart
Journal of Statistical Mechanics Theory and Experiment (2024) Vol. 2024, Iss. 10, pp. 104020-104020
Open Access | Times Cited: 1
Learning sparse features can lead to overfitting in neural networks *
Leonardo Petrini, Francesco Cagnetta, Eric Vanden‐Eijnden, et al.
Journal of Statistical Mechanics Theory and Experiment (2023) Vol. 2023, Iss. 11, pp. 114003-114003
Open Access | Times Cited: 3
Statistical Signatures of Abstraction in Deep Neural Networks
Carlo Orientale Caputo, Matteo Marsili
(2024)
Open Access
Parallel development of object recognition in newborn chicks and deep neural networks
Lalit Pandey, Donsuk Lee, Samantha M. W. Wood, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 12, pp. e1012600-e1012600
Open Access
How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
Francesco Cagnetta, Leonardo Petrini, Umberto Maria Tomasini, et al.
arXiv (Cornell University) (2023)
Open Access | Times Cited: 1
Observing Schrödinger's Cat with Artificial Intelligence: Emergent Classicality from Information Bottleneck
Zhelun Zhang, Yi‐Zhuang You
arXiv (Cornell University) (2023)
Open Access
State space search revisited from perspective of deep learning
Zhengxin Chen
Procedia Computer Science (2023) Vol. 221, pp. 227-231
Open Access
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