Toward Symbolic Regression Based Model Transform for Convolutional Neural Network

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@InProceedings{seo:2023:GECCOcomp,

• author = "Kisung Seo and Seok Beom Roh and Soonyong Gwon",
• title = "Toward Symbolic Regression Based Model Transform for Convolutional Neural Network",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Justyna Petke and Aniko Ekart",
• pages = "81--82",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, ResNet18, symbolic regression, CIFAR-10, convolutional neural network",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3596942",
• size = "2 pages",
• abstract = "This paper introduces a symbolic regression based filter transform for convolutional neural network using CGP (Cartesian Genetic Programming). Symbolic regression is a powerful technique to discover analytic equations that describe data, which can lead to explainable models and the ability to predict unseen data. In contrast, neural networks have achieved amazing levels of accuracy on image recognition and natural language processing tasks, but they are often seen as black-box models that are difficult to interpret and typically extrapolate poorly. symbolic regression approaches to deep learning are under explored.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Kisung Seo Seok Beom Roh Soonyong Gwon

Citations