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.
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/).
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.
1 code implementation • 7 May 2020 • Guodong Rong, Byung Hyun Shin, Hadi Tabatabaee, Qiang Lu, Steve Lemke, Mārtiņš Možeiko, Eric Boise, Geehoon Uhm, Mark Gerow, Shalin Mehta, Eugene Agafonov, Tae Hyung Kim, Eric Sterner, Keunhae Ushiroda, Michael Reyes, Dmitry Zelenkovsky, Seonman Kim
Testing autonomous driving algorithms on real autonomous vehicles is extremely costly and many researchers and developers in the field cannot afford a real car and the corresponding sensors.
no code implementations • 17 Mar 2020 • Daniel J. Fremont, Edward Kim, Yash Vardhan Pant, Sanjit A. Seshia, Atul Acharya, Xantha Bruso, Paul Wells, Steve Lemke, Qiang Lu, Shalin Mehta
We present a new approach to automated scenario-based testing of the safety of autonomous vehicles, especially those using advanced artificial intelligence-based components, spanning both simulation-based evaluation as well as testing in the real world.
no code implementations • 12 May 2017 • Qiang Lu, Kyoung-Dae Kim
Then, we show that the basic DICA has a computational complexity of $\mathcal{O}(n^2 L_m^3)$ where $n$ is the number of vehicles granted to cross an intersection and $L_m$ is the maximum length of intersection crossing routes.
no code implementations • 14 May 2013 • Xiao-Bo Jin, Qiang Lu, Feng Wang, Quan-gong Huo
The study focused on the machine learning analysis approaches to identify the adulteration of 9 kinds of edible oil qualitatively and answered the following three questions: Is the oil sample adulterant?