Prove Symbolic Regression is NP-hard by Symbol Graph

no code implementations • 22 Apr 2024 • Jinglu Song, Qiang Lu, Bozhou Tian, Jingwen Zhang, Jake Luo, Zhiguang Wang

Symbolic regression (SR) is the task of discovering a symbolic expression that fits a given data set from the space of mathematical expressions.

regression Symbolic Regression

Taylor Genetic Programming for Symbolic Regression

no code implementations • 28 Apr 2022 • Baihe He, Qiang Lu, Qingyun Yang, Jake Luo, Zhiguang Wang

So, the search process of GP is usually slow, and the final results could be unstable. To guide GP by these characteristics, we propose a new method for SR, called Taylor genetic programming (TaylorGP) (Code and appendix at https://kgae-cup. github. io/TaylorGP/).

regression Symbolic Regression

Exploring Hidden Semantics in Neural Networks with Symbolic Regression

no code implementations • 22 Apr 2022 • Yuanzhen Luo, Qiang Lu, Xilei Hu, Jake Luo, Zhiguang Wang

It then leverages a multi-chromosome NNCGP to represent hidden semantics of all layers of the NN.

regression Symbolic Regression

A Deep Learning Study on Osteosarcoma Detection from Histological Images

no code implementations • 2 Nov 2020 • D M Anisuzzaman, Hosein Barzekar, Ling Tong, Jake Luo, Zeyun Yu

In this study, transfer learning techniques, pre-trained CNNs, are adapted to a public dataset on osteosarcoma histological images to detect necrotic images from non-necrotic and healthy tissues.

Transfer Learning whole slide images