10 citations · 33 across the 23 of their papers we have counts for
11 papers · 1 filter
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…
Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System
Zhiwen Yu, Derong Yang, Liujian Zhang +5
Partial differential equations (PDEs) play a central role in modeling complex physical, biological, and engineering systems. While traditional numerical solvers are robust, they of…
\textsc{Lethe}: Principled Dual-Stream Update for Persistent Knowledge Erasure in Federated Unlearning
Wentai Wu, Hanwei Tan, Yijun Quan +4
Federated unlearning (FU) aims to erase knowledge from a global model. Existing studies commonly assume that federated collaboration terminates after unlearning, overlooking a depl…
Mitigating Sample-Level Imbalance via Probabilistic Separation for Adaptive Multimodal Fusion
Zhiwen Yu, Zhaocheng Liu, Xiaoqing Liu +2
Multimodal learning faces modality imbalance, where dominant modalities suppress weaker ones due to inconsistent convergence rates. Existing static or heuristic methods overlook sa…
Diffusion Disambiguation Models for Partial Label Learning
Jinfu Fan, Xiaohui Zhong, Kangrui Ren +4
Learning from ambiguous labels is a long-standing problem in practical machine learning applications. The purpose of \emph{partial label learning} (PLL) is to identify the ground-t…
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…