Foundational optimization algorithms are the core driving force behind deep learning, evolving from early stochastic gradient descent (SGD) to the widely adopted Adam family. However, as the scale of ...

The path planning capability of autonomous robots in complex environments is crucial for their widespread application in the real world. However, long-term decision-making and sparse reward signals ...

The Snow Goose Algorithm (SGA) is a new meta-heuristic algorithm proposed in 2024, which has been proved to have good optimization effect, but there are still problems that are easy to fall into local ...

This repository provides an official CUDA/cuDNN-accelerated extension of the Neural Network Libraries deep learning framework. See Mixed precision training tutorial for a stable training technique ...

Numerous issues and challenges in today's science, industry, and technology can be defined as optimization problems. All optimization problems have three parts: an objective function, constraints, and ...

1 College of Railway Transportation, Hunan University of Technology, Zhuzhou, China. 2 Jiangsu Shengtong Electric New Energy Technology Co., Ltd., Changsha, China. 3 ...

The past decade has been marked with a proliferation of community detection algorithms that aim to organize nodes (e.g., individuals, brain regions, variables) into modular structures that indicate ...