5 citations · 9 across the 5 of their papers we have counts for
6 papers
HiER-BLS: A Hierarchy-Guided and Error-Correcting Robust Incremental Broad Learning System
Gongli Zhang, C. L. Philip Chen, Zhulin Liu
Broad Learning System (BLS) supports analytical training and incremental expansion, but its growth needs guidance on which inputs new blocks should learn from. Weight errors pose a…
Share First, Route What Remains: A Unified Framework for Token-Adaptive MoE Computation
Gongli Zhang, Zhulin Liu, C. L. Philip Chen
Mixture-of-experts (MoE) models have recently moved beyond routing a fixed number of complete experts. Shared-expert designs preserve reusable knowledge, fine-grained methods vary…
Incremental Self-training for Semi-supervised Learning
Jifeng Guo, Zhulin Liu, Tong Zhang +1
Semi-supervised learning provides a solution to reduce the dependency of machine learning on labeled data. As one of the efficient semi-supervised techniques, self-training (ST) ha…
Siamese Labels Auxiliary Learning
Wenrui Gan, Zhulin Liu, C. L. Philip Chen +1
In deep learning, auxiliary training has been widely used to assist the training of models. During the training phase, using auxiliary modules to assist training can improve the pe…
Reducing the Computational Complexity of Pseudoinverse for the Incremental Broad Learning System on Added Inputs
Hufei Zhu, Zhulin Liu, C. L. Philip Chen +1
In this brief, we improve the Broad Learning System (BLS) [7] by reducing the computational complexity of the incremental learning for added inputs. We utilize the inverse of a sum…
Approximation learning methods of Harmonic Mappings in relation to Hardy Spaces
Zhulin Liu, C. L. Philip Chen
A new Hardy space Hardy space approach of Dirichlet type problem based on Tikhonov regularization and Reproducing Hilbert kernel space is discussed in this paper, which turns out t…