47 citations · 102 across the 12 of their papers we have counts for
12 papers
Class Incremental Fault Diagnosis under Limited Fault Data via Supervised Contrastive Knowledge Distillation
Hanrong Zhang, Yifei Yao, Zixuan Wang +4
Class-incremental fault diagnosis requires a model to adapt to new fault classes while retaining previous knowledge. However, limited research exists for imbalanced and long-tailed…
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
Mengxuan Li, Ke Liu, Hongyang Chen +3
Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this t…
Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model
Zhonghan Zhao, Ke Ma, Wenhao Chai +6
With the power of large language models (LLMs), open-ended embodied agents can flexibly understand human instructions, generate interpretable guidance strategies, and output execut…
Empirical Study of Zero-Shot NER with ChatGPT
Tingyu Xie, Qi Li, Jian Zhang +3
Large language models (LLMs) exhibited powerful capability in various natural language processing tasks. This work focuses on exploring LLM performance on zero-shot information ext…
Generalized Out-of-distribution Fault Diagnosis (GOOFD) via Internal Contrastive Learning
Xingyue Wang, Hanrong Zhang, Xinlong Qiao +4
Fault diagnosis is crucial in monitoring machines within industrial processes. With the increasing complexity of working conditions and demand for safety during production, diverse…
Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis
Zixuan Wang, Bo Qin, Mengxuan Li +4
The efficient utilization of wind power by wind turbines relies on the ability of their pitch systems to adjust blade pitch angles in response to varying wind speeds. However, the…
Deep Learning Methods for Small Molecule Drug Discovery: A Survey
Wenhao Hu, Yingying Liu, Xuanyu Chen +4
With the development of computer-assisted techniques, research communities including biochemistry and deep learning have been devoted into the drug discovery field for over a decad…
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
Mengxuan Li, Peng Peng, Jingxin Zhang +2
In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely…
An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis
Mengxuan Li, Peng Peng, Min Wang +1
Fault detection and diagnosis is significant for reducing maintenance costs and improving health and safety in chemical processes. Convolution neural network (CNN) is a popular dee…
SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data
Peng Peng, Hanrong Zhang, Mengxuan Li +3
Intelligent fault diagnosis has made extraordinary advancements currently. Nonetheless, few works tackle class-incremental learning for fault diagnosis under limited fault data, i.…
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering
Chenlu Zhan, Peng Peng, Hongsen Wang +2
Medical Visual Question Answering (Medical-VQA) aims to to answer clinical questions regarding radiology images, assisting doctors with decision-making options. Nevertheless, curre…
Supervised Contrastive Learning with Tree-Structured Parzen Estimator Bayesian Optimization for Imbalanced Tabular Data
Shuting Tao, Peng Peng, Qi Li +1
Class imbalance has a detrimental effect on the predictive performance of most supervised learning algorithms as the imbalanced distribution can lead to a bias preferring the major…