1 code implementation • 24 Aug 2023 • Mai Peng, Zeneng She, Delaram Yazdani, Danial Yazdani, Wenjian Luo, Changhe Li, Juergen Branke, Trung Thanh Nguyen, Amir H. Gandomi, Yaochu Jin, Xin Yao
In this paper, to assist researchers in performing experiments and comparing their algorithms against several EDOAs, we develop an open-source MATLAB platform for EDOAs, called Evolutionary Dynamic Optimization LABoratory (EDOLAB).
no code implementations • 25 May 2022 • Chong Ma, Lin Zhao, Yuzhong Chen, Lu Zhang, Zhenxiang Xiao, Haixing Dai, David Liu, Zihao Wu, Zhengliang Liu, Sheng Wang, Jiaxing Gao, Changhe Li, Xi Jiang, Tuo Zhang, Qian Wang, Dinggang Shen, Dajiang Zhu, Tianming Liu
To address this problem, we propose to infuse human experts' intelligence and domain knowledge into the training of deep neural networks.
no code implementations • 21 May 2022 • Li Yang, Zhibin He, Changhe Li, Junwei Han, Dajiang Zhu, Tianming Liu, Tuo Zhang
The convolution on mesh considers the spatial organization of functional gradients and folding patterns on a cortical sheet and the newly designed channel attention block enhances the interpretability of the contribution of different functional gradients to cortical folding prediction.
no code implementations • 20 May 2022 • Yuzhong Chen, Zhenxiang Xiao, Lin Zhao, Lu Zhang, Haixing Dai, David Weizhong Liu, Zihao Wu, Changhe Li, Tuo Zhang, Changying Li, Dajiang Zhu, Tianming Liu, Xi Jiang
However, for data-intensive models such as vision transformer (ViT), current fine-tuning based FSL approaches are inefficient in knowledge generalization and thus degenerate the downstream task performances.
no code implementations • 3 Jan 2022 • Wenjian Luo, Xin Lin, Changhe Li, Shengxiang Yang, Yuhui Shi
This is very helpful for the decision makers, especially when facing changing environments.
1 code implementation • 11 Jun 2021 • Danial Yazdani, Michalis Mavrovouniotis, Changhe Li, Wenjian Luo, Mohammad Nabi Omidvar, Amir H. Gandomi, Trung Thanh Nguyen, Juergen Branke, XiaoDong Li, Shengxiang Yang, Xin Yao
This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization.