no code implementations • 25 May 2023 • Jian-Nan Su, Min Gan, Guang-Yong Chen, Wenzhong Guo, C. L. Philip Chen
Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution.
no code implementations • 12 May 2023 • Min Gan, Xiang-xiang Su, Guang-Yong Chen, Jing Chen
In one routine of the proposed algorithm, the linear parameters are updated by the recursive least squares (RLS) algorithm, which is equivalent to a stochastic Newton method; then, based on the updated linear parameters, the nonlinear parameters are updated by the stochastic gradient method (SGD).
no code implementations • 3 Apr 2023 • Guang-Yong Chen, Yong-Hang Yu, Min Gan, C. L. Philip Chen, Wenzhong Guo
Random functional-linked types of neural networks (RFLNNs), e. g., the extreme learning machine (ELM) and broad learning system (BLS), which avoid suffering from a time-consuming training process, offer an alternative way of learning in deep structure.
no code implementations • 14 Jan 2023 • Jinyang Wang, Tao Wang, Min Gan, George Hadjichristofi
Deep convolutional neural networks have been widely used in scene classification of remotely sensed images.
1 code implementation • 2 Dec 2022 • Jian-Nan Su, Min Gan, Guang-Yong Chen, Jia-Li Yin, C. L. Philip Chen
Utilizing this finding, we proposed a Global Learnable Attention (GLA) to adaptively modify similarity scores of non-local textures during training instead of only using a fixed similarity scoring function such as the dot product.
no code implementations • 23 Jul 2021 • Bowen Hu, Baiying Lei, Shuqiang Wang, Yong liu, BingChuan Wang, Min Gan, Yanyan Shen
A branching predictor and several hierarchical attention pipelines are constructed to generate point clouds that accurately describe the incomplete images and then complete these point clouds with high quality.