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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:

The Gap between Theory and Practice in Function Approximation with Deep Neural Networks
Ben Adcock, Nick Dexter
SIAM Journal on Mathematics of Data Science (2021) Vol. 3, Iss. 2, pp. 624-655
Open Access | Times Cited: 104

Showing 1-25 of 104 citing articles:

The Modern Mathematics of Deep Learning
Julius Berner, Philipp Grohs, Gitta Kutyniok, et al.
Cambridge University Press eBooks (2022), pp. 1-111
Open Access | Times Cited: 103
The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale’s 18th problem
Matthew J. Colbrook, Vegard Antun, Anders C. Hansen
Proceedings of the National Academy of Sciences (2022) Vol. 119, Iss. 12
Open Access | Times Cited: 102
Mathematical Aspects of Deep Learning
Philipp Grohs, Philipp Grohs, Julius Berner, et al.
Cambridge University Press eBooks (2022)
Open Access | Times Cited: 51
Convergence of deep convolutional neural networks
Yuesheng Xu, Haizhang Zhang
Neural Networks (2022) Vol. 153, pp. 553-563
Open Access | Times Cited: 39
Developing a physics-informed and physics-penalized neural network model for preliminary design of multi-stage friction pendulum bearings
Ahed Habib, Umut Yıldırım
Engineering Applications of Artificial Intelligence (2022) Vol. 113, pp. 104953-104953
Closed Access | Times Cited: 32
Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces
Philipp Grohs, Felix Voigtlaender
Foundations of Computational Mathematics (2023) Vol. 24, Iss. 4, pp. 1085-1143
Open Access | Times Cited: 20
Compressive Imaging: Structure, Sampling, Learning
Ben Adcock, Anders C. Hansen
(2021)
Open Access | Times Cited: 36
RandONets: Shallow Networks with Random Projections for Learning Linear and Nonlinear Operators
Gianluca Fabiani, Ioannis G. Kevrekidis, Constantinos Siettos, et al.
Journal of Computational Physics (2024), pp. 113433-113433
Open Access | Times Cited: 6
Limitations of Deep Learning for Inverse Problems on Digital Hardware
Holger Boche, Adalbert Fono, Gitta Kutyniok
IEEE Transactions on Information Theory (2023) Vol. 69, Iss. 12, pp. 7887-7908
Open Access | Times Cited: 13
Visualizing high-dimensional loss landscapes with Hessian directions
Lucas Böttcher, Gregory S. Wheeler
Journal of Statistical Mechanics Theory and Experiment (2024) Vol. 2024, Iss. 2, pp. 023401-023401
Open Access | Times Cited: 4
Adaptive quadratures for nonlinear approximation of low-dimensional PDEs using smooth neural networks
Alexandre Magueresse, Santiago Badia
Computers & Mathematics with Applications (2024) Vol. 162, pp. 1-21
Open Access | Times Cited: 4
Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression
Behzad Azmi, Dante Kalise, Karl Kunisch
arXiv (Cornell University) (2020)
Open Access | Times Cited: 28
Constructive Deep ReLU Neural Network Approximation
Lukas Herrmann, Joost A. A. Opschoor, Christoph Schwab
Journal of Scientific Computing (2022) Vol. 90, Iss. 2
Closed Access | Times Cited: 19
Machine Learning Optimized Orthogonal Basis Piecewise Polynomial Approximation
Hannes Waclawek, Stefan Huber
Lecture notes in computer science (2025), pp. 427-441
Closed Access
The Troublesome Kernel: On Hallucinations, No Free Lunches, and the Accuracy-Stability Tradeoff in Inverse Problems
Nina Maria Gottschling, Vegard Antun, Anders C. Hansen, et al.
SIAM Review (2025) Vol. 67, Iss. 1, pp. 73-104
Closed Access
Random Projection Neural Networks of Best Approximation: Convergence Theory and Practical Applications
Gianluca Fabiani
SIAM Journal on Mathematics of Data Science (2025) Vol. 7, Iss. 2, pp. 385-409
Open Access
Data-Driven Methods and Adaptive Control: Stochastic Analysis
Andrew J. Kurdila, Andrea L’Afflitto, John A. Burns
Lecture notes in control and information sciences (2025), pp. 269-315
Closed Access
On Efficient Algorithms for Computing Near-Best Polynomial Approximations to High-Dimensional, Hilbert-Valued Functions from Limited Samples
Ben Adcock, Simone Brugiapaglia, Nick Dexter, et al.
Memoirs of the European Mathematical Society (2024)
Open Access | Times Cited: 3
Residual multi-fidelity neural network computing
Owen Davis, Mohammad Motamed, Raúl Tempone
BIT Numerical Mathematics (2025) Vol. 65, Iss. 2
Closed Access
Mathematical algorithm design for deep learning under societal and judicial constraints: The algorithmic transparency requirement
Holger Boche, Adalbert Fono, Gitta Kutyniok
Applied and Computational Harmonic Analysis (2025), pp. 101763-101763
Open Access
Neural Networks and Deep Learning
Ben Adcock, Anders C. Hansen
Cambridge University Press eBooks (2021), pp. 431-457
Closed Access | Times Cited: 19
Numerical solution of ruin probability of continuous time model based on optimal adaptive particle swarm optimization-triangular neural network algorithm
Xu Yiming, Xinyue Fan, Yunlei Yang, et al.
Soft Computing (2023) Vol. 27, Iss. 19, pp. 14321-14335
Closed Access | Times Cited: 7
Friction modelling and the use of a physics-informed neural network for estimating frictional torque characteristics
Paweł Olejnik, Samuel Ayankoso
Meccanica (2023)
Open Access | Times Cited: 7
Learning smooth functions in high dimensions
Ben Adcock, Simone Brugiapaglia, Nick Dexter, et al.
Handbook of numerical analysis (2024), pp. 1-52
Closed Access | Times Cited: 2
Artificial-Intelligence-Based Condition Monitoring of Industrial Collaborative Robots: Detecting Anomalies and Adapting to Trajectory Changes
Samuel Ayankoso, Fengshou Gu, Hassna Louadah, et al.
Machines (2024) Vol. 12, Iss. 9, pp. 630-630
Open Access | Times Cited: 2
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