Open Access
| Issue |
ITM Web Conf.
Volume 59, 2024
II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|
|
|---|---|---|
| Article Number | 04010 | |
| Number of page(s) | 7 | |
| Section | Adaptive Intelligence: Exploring Learning in Evolutionary Algorithms and Neural Networks | |
| DOI | https://doi.org/10.1051/itmconf/20245904010 | |
| Published online | 25 January 2024 | |
- S. Gonzalez, R. Miikkulainen, Optimizing loss functions through multi-variate taylor polynomial parameterization, Proceedings of the Genetic and Evolutionary Computation Conference (2021) [Google Scholar]
- J.T. Springenberg, A. Dosovitskiy, T. Brox, M.A. Riedmiller, Striving for Simplicity: The All Convolutional Net. CoRR, abs/1412.6806 (2014) [Google Scholar]
- G. Bingham, W. Macke, R. Miikkulainen, Evolutionary optimization of deep learning activation functions, Proceedings of the 2020 Genetic and Evolutionary Computation Conference (2020) [Google Scholar]
- A.I. Tikhonov. Solution of Incorrectly Formulated Problems and the Regularization Method (1963) [Google Scholar]
- S. Gonzalez, R. Miikkulainen, Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization, 2020 IEEE Congress on Evolutionary Computation (CEC), 1–8 (2019) [Google Scholar]
- Deb, Kalyanmoy et al., A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II, Parallel Problem Solving from Nature (2000) [Google Scholar]
- Storn Rainer, Kenneth V. Price, Journal of Global Optimization 11, 341–359 (1997) [CrossRef] [Google Scholar]
- L. Deng, IEEE Signal Processing Magazine 29(6), 141–142 (2012) [CrossRef] [Google Scholar]
- Alex Krizhevsky, Vinod Nair, Geoffrey Hinton "The CIFAR-10 dataset " (2014). http://www.cs.toronto.edu/kriz/cifar.html 55.5. [Google Scholar]
- Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng Reading Digits in Natural Images with Unsupervised Feature Learning NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2011) [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.