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Found 6 papers, 1 papers with code
Taylor Genetic Programming for Symbolic Regression
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/).
Exploring Hidden Semantics in Neural Networks with Symbolic Regression
It then leverages a multi-chromosome NNCGP to represent hidden semantics of all layers of the NN.
LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving
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.
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World
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.
Autonomous and Connected Intersection Crossing Traffic Management using Discrete-Time Occupancies Trajectory
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.
Qualitative detection of oil adulteration with machine learning approaches
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?
