Trusted Multi-View Classification with Expert Knowledge Constraints

Xinyan Liang, Shijie Wang, Yuhua Qian, Qian Guo, Liang Du, Bingbing Jiang, Tingjin Luo, Feijiang Li

Multi-view classification (MVC) based on the Dempster-Shafer theory has gained significant recognition for its reliability in safety-critical applications.

ICML' 2025

Robust Automatic Modulation Classification with Fuzzy Regularization

Xinyan Liang, Ruijie Sang, Yuhua Qian, Qian Guo, Feijiang Li，Liang Du

Automatic Modulation Classification (AMC) serves as a foundational pillar for cognitive radio systems, enabling critical functionalities including dynamic spectrum allocation, non-cooperative signal surveillance

ICML' 2025

Stabilizing Sample Similarity in Representation via Mitigating Random Consistency

Jieting Wang, ZhangZelong, Feijiang Li, Yuhua Qian, Xinyan Liang

Deep learning has been widely applied due to its powerful representation ability. Intuitively, the quality of representation ability can be measured by sample similarity.

ICML' 2025

A Multi-view Fusion Approach for Enhancing Speech Signals via Short-time Fractional Fourier Transform

Zikun Jin, Yuhua Qian, Xinyan Liang, Haijun Geng

​Deep learning-based speech enhancement (SE) methods focus on reconstructing speech from the time or frequency domain.

IJCAI' 2025

View-Association-Guided Dynamic Multi-View Classification

Xinyan Liang, Li Lv, Qian Guo, Bingbing Wang, Feijiang Li, Liang Du, Lu Chen

​In multi-view classification tasks, integrating information from multiple views effectively is crucial for improving model performance.

IJCAI' 2025

An Association-based Fusion Method for Speech Enhancement

Shijie Wang, Qian Guo, Lu Chen, Liang Du, Zikun Jin, Zhian yuan, Xinyan Liang

Deep learning-based speech enhancement (SE) methods predominantly draw upon two architectural frameworks: generative adversarial networks and diffusion models.

IJCAI' 2025

A Fast Neural Architecture Search Method for Multi-Modal Classification via Knowledge Sharing

Zhihua Cui, Shiwu Sun, Qian Guo, Xinyan Liang, Yuhua Qian, Zhixia Zhang

Neural architecture search-based multi-modal classification (NAS-MMC) aims to automatically find optimal network structures for improving the multi-modal classification performance.

IJCAI' 2025

Multi-Scale Hyperbolic Contrastive Learning for Cross-Subject EEG Emotion Recognition

Jiang Chang, Zhixin Zhang, Yuhua Qian, Pan Lin

Electroencephalography (EEG) serves as a reliable and objective signal for affective computing applications. However, individual differences in EEG signals pose a significant challenge for emotion recognition tasks across subjects.

IEEE Transactions on Affective Computing

Trusted Multi-View Classification via Evolutionary Multi-View Fusion

Xinyan Liang, Pinhan Fu, Yuhua Qian, Qian Guo, Guoqing Liu

Multi-view classification based on the Dempster-Shafer theory is widely recognized for its reliability in safety-critical domains with multi-view data.

ICLR 2025