SNR: Symbolic network-based rectifiable learning framework for symbolic regression-Reference-Cited by-同舟云学术

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SNR: Symbolic network-based rectifiable learning framework for symbolic regression

Published:2023-08 Issue: Volume:165 Page:1021-1034
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Liu Jingyi^ORCID,Li Weijun^ORCID,Yu Lina^ORCID,Wu Min^ORCID,Sun Linjun^ORCID,Li Wenqiang^ORCID,Li Yanjie^ORCID

Funder

Chinese Academy of Sciences

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference35 articles.

1. Multiple regression genetic programming;Arnaldo,2014

2. Synthesis of adder circuit using cartesian genetic programming;Asha;Middle-East Journal of Scientific Research,2015

3. Evolutionary computation 1: Basic algorithms and operators;Bäck,2018

4. Contemporary symbolic regression methods and their relative performance;Cava,2021

5. Rethinking the inception architecture for computer vision;Christian,2016

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1. Camo: Capturing the Modularity by End-to-End Models for Symbolic Regression;2024

2. Artificial Neural Network-Based Approach to Modeling Energy Bands of GaN-Based Heterojunction Materials;2023 International Conference on High Performance Big Data and Intelligent Systems (HDIS);2023-12-06

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