Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Understanding deep convolutional networks
Stéphane Mallat
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2016) Vol. 374, Iss. 2065, pp. 20150203-20150203
Open Access | Times Cited: 488

Showing 1-25 of 488 citing articles:

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, George Em Karniadakis
Journal of Computational Physics (2018) Vol. 378, pp. 686-707
Open Access | Times Cited: 9674
Physics-informed machine learning
George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, et al.
Nature Reviews Physics (2021) Vol. 3, Iss. 6, pp. 422-440
Closed Access | Times Cited: 3520
Geometric Deep Learning: Going beyond Euclidean data
Michael M. Bronstein, Joan Bruna, Yann LeCun, et al.
IEEE Signal Processing Magazine (2017) Vol. 34, Iss. 4, pp. 18-42
Open Access | Times Cited: 3197
Safety Verification of Deep Neural Networks
Xiaowei Huang, Marta Kwiatkowska, Sen Wang, et al.
Lecture notes in computer science (2017), pp. 3-29
Closed Access | Times Cited: 828
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
Sifan Wang, Yujun Teng, Paris Perdikaris
SIAM Journal on Scientific Computing (2021) Vol. 43, Iss. 5, pp. A3055-A3081
Closed Access | Times Cited: 809
Convolutional Neural Networks for Inverse Problems in Imaging: A Review
Michael T. McCann, Kyong Hwan Jin, Michaël Unser
IEEE Signal Processing Magazine (2017) Vol. 34, Iss. 6, pp. 85-95
Open Access | Times Cited: 702
Solving inverse problems using data-driven models
Simon Arridge, Peter Maaß, Ozan Öktem, et al.
Acta Numerica (2019) Vol. 28, pp. 1-174
Open Access | Times Cited: 553
Deep learning for universal linear embeddings of nonlinear dynamics
Bethany Lusch, J. Nathan Kutz, Steven L. Brunton
Nature Communications (2018) Vol. 9, Iss. 1
Open Access | Times Cited: 506
Universality of deep convolutional neural networks
Ding‐Xuan Zhou
Applied and Computational Harmonic Analysis (2019) Vol. 48, Iss. 2, pp. 787-794
Open Access | Times Cited: 461
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
Grace W. Lindsay
Journal of Cognitive Neuroscience (2020) Vol. 33, Iss. 10, pp. 2017-2031
Open Access | Times Cited: 445
Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution
Huaibo Huang, Ran He, Zhenan Sun, et al.
(2017), pp. 1698-1706
Closed Access | Times Cited: 431
Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification
Chen Lü, Zhenya Wang, Bo Zhou
Advanced Engineering Informatics (2017) Vol. 32, pp. 139-151
Closed Access | Times Cited: 393
Deep learning of vortex-induced vibrations
Maziar Raissi, Zhicheng Wang, Michael S. Triantafyllou, et al.
Journal of Fluid Mechanics (2018) Vol. 861, pp. 119-137
Open Access | Times Cited: 366
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning
Amin Azmoodeh, Ali Dehghantanha, Kim‐Kwang Raymond Choo
IEEE Transactions on Sustainable Computing (2018) Vol. 4, Iss. 1, pp. 88-95
Open Access | Times Cited: 353
Deep convolution neural network for image recognition
Boukaye Boubacar Traoré, Bernard Kamsu-Foguem, Fana Tangara
Ecological Informatics (2018) Vol. 48, pp. 257-268
Open Access | Times Cited: 351
Adversarial Perturbations Against Deep Neural Networks for Malware Classification
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, et al.
arXiv (Cornell University) (2016)
Open Access | Times Cited: 334
What makes ImageNet good for transfer learning
Minyoung Huh, Pulkit Agrawal, Alexei A. Efros
arXiv (Cornell University) (2016)
Closed Access | Times Cited: 328
Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
Zhice Fang, Yi Wang, Ling Peng, et al.
Computers & Geosciences (2020) Vol. 139, pp. 104470-104470
Closed Access | Times Cited: 268
Flood susceptibility mapping using convolutional neural network frameworks
Yi Wang, Zhice Fang, Haoyuan Hong, et al.
Journal of Hydrology (2019) Vol. 582, pp. 124482-124482
Closed Access | Times Cited: 260
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton, J. Nathan Kutz
(2019)
Closed Access | Times Cited: 245
Deep Learning: An Introduction for Applied Mathematicians
Catherine F. Higham, Desmond J. Higham
SIAM Review (2019) Vol. 61, Iss. 4, pp. 860-891
Open Access | Times Cited: 237
An empirical study on evaluation metrics of generative adversarial networks
Qiantong Xu, Gao Huang, Yuan Yang, et al.
arXiv (Cornell University) (2018)
Open Access | Times Cited: 215
Seeing Voices and Hearing Faces: Cross-Modal Biometric Matching
Arsha Nagrani, Samuel Albanie, Andrew Zisserman
(2018)
Open Access | Times Cited: 213
Theory of deep convolutional neural networks: Downsampling
Ding‐Xuan Zhou
Neural Networks (2020) Vol. 124, pp. 319-327
Closed Access | Times Cited: 204
Learning to predict the cosmological structure formation
Siyu He, Yin Li, Yu Feng, et al.
Proceedings of the National Academy of Sciences (2019) Vol. 116, Iss. 28, pp. 13825-13832
Open Access | Times Cited: 193
Page 1 - Next Page

Scroll to top