Mengxuan Li
Orcid: 0000-0001-7278-7891Affiliations:
- Zhejiang University, College of Computer Science and Technology, Hangzhou, China
According to our database1,
Mengxuan Li
authored at least 11 papers
between 2022 and 2026.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2026
CSTSINR: improving temporal continuity via convolutional structured implicit neural representations for time series anomaly detection.
Neural Networks, 2026
2025
Class Incremental Fault Diagnosis Under Limited Fault Data via Supervised Contrastive Knowledge Distillation.
IEEE Trans. Ind. Informatics, June, 2025
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection.
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025
ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data.
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025
2024
An Order-Invariant and Interpretable Dilated Convolution Neural Network for Chemical Process Fault Detection and Diagnosis.
IEEE Trans Autom. Sci. Eng., July, 2024
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-Hoc Interpretable Fault Diagnosis With Limited Fault Samples.
IEEE Trans. Neural Networks Learn. Syst., May, 2024
2023
Hard Sample Mining Enabled Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis.
CoRR, 2023
An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis.
CoRR, 2023
SCLIFD: Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data.
CoRR, 2023
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples.
CoRR, 2023
2022
Enabling Improved Learning Capability of Industrial Robots with Knowledge Graph Towards Intelligent Digital Twins.
Proceedings of the 25th IEEE International Conference on Computer Supported Cooperative Work in Design, 2022