Open Access
Issue
ITM Web Conf.
Volume 64, 2024
2nd International Conference on Applied Computing & Smart Cities (ICACS24)
Article Number 01003
Number of page(s) 7
DOI https://doi.org/10.1051/itmconf/20246401003
Published online 05 July 2024
  1. Li, Mingchen, Mahdi Soltanolkotabi, and Samet Oymak. “Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks.” International conference on artificial intelligence and statistics. PMLR, (2020). [Google Scholar]
  2. Rice, Leslie, Eric Wong, and Zico Kolter. “Overfitting in adversarially robust deep learning.” International conference on machine learning. PMLR, (2020). [Google Scholar]
  3. Prechelt, Lutz. “Early stopping-but when?.” Neural Networks: Tricks of the trade. Berlin, Heidelberg: Springer Berlin Heidelberg, 55–69, (2002). [Google Scholar]
  4. Ji, Ziwei, Justin Li, and Matus Telgarsky. “Early-stopped neural networks are consistent.” Advances in Neural Information Processing Systems 34 (2021): 18051817. [Google Scholar]
  5. Li, Zewen, et al. “A survey of convolutional neural networks: analysis, applications, and prospects.” IEEE transactions on neural networks and learning systems 33.12 (2021): 6999–7019. [Google Scholar]
  6. M. Elgendy, Deep Learning for Vision Systems, October (2020). [Google Scholar]
  7. Zhang, Yu, et al. “A survey on neural network interpretability.” IEEE Transactions on Emerging Topics in Computational Intelligence 5.5 (2021): 726–742. [CrossRef] [Google Scholar]
  8. Xia, Xiaobo, et al. “Robust early-learning: Hindering the memorization of noisy labels.” International conference on learning representations. (2020). [Google Scholar]
  9. Shen, Ruoqi, Liyao Gao, and Yi-An Ma. “On optimal early stopping: Over-informative versus under-informative parametrization.” arXiv preprint arXiv:2202.09885 (2022). [Google Scholar]
  10. Ferro, Manuel Vilares, et al. “Early stopping by correlating online indicators in neural networks.” Neural Networks 159 (2023): 109–124. [CrossRef] [Google Scholar]
  11. Agliari, Elena, et al. “Regularization, early-stopping and dreaming: a Hopfield-like setup to address generalization and overfitting.” arXiv preprint arXiv:2308.01421 (2023). [Google Scholar]
  12. Miseta, Tamás, Attila Fodor, and Ágnes Vathy-Fogarassy. “Surpassing early stopping: A novel correlation-based stopping criterion for neural networks.” Neurocomputing 567 (2024): 127028. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.