Search Results for author: Junjie Yu

Found 7 papers, 4 papers with code

Data-driven Inverter-based Volt/VAr Control for Partially Observable Distribution Networks

no code implementations31 Jul 2020 Tong Xu, Wenchuan Wu, Yiwen Hong, Junjie Yu, Fazhong Zhang

To provide a practical Volt/Var control (VVC) strategy for such networks, a data-driven VVC method is proposed in this paper.

regression

Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction

1 code implementation COLING 2020 Tong Zhu, Haitao Wang, Junjie Yu, Xiabing Zhou, Wenliang Chen, Wei zhang, Min Zhang

The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different.

Relation Relation Extraction

Improving Relation Extraction with Relational Paraphrase Sentences

1 code implementation COLING 2020 Junjie Yu, Tong Zhu, Wenliang Chen, Wei zhang, Min Zhang

In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.

Relation Relation Extraction

Embedding Decomposition for Artifacts Removal in EEG Signals

1 code implementation2 Dec 2021 Junjie Yu, Chenyi Li, Kexin Lou, Chen Wei, Quanying Liu

DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal.

Denoising EEG +1

STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction

1 code implementation COLING 2022 Junjie Yu, Xing Wang, Jiangjiang Zhao, Chunjie Yang, Wenliang Chen

The approach first classifies the auto-annotated instances into two groups: confident instances and uncertain instances, according to the probabilities predicted by a teacher model.

Relation Relation Extraction

Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration

no code implementations5 Apr 2024 Xudong Guo, Daming Shi, Junjie Yu, Wenhui Fan

Second, we introduce a heterogeneous layer for decision-making, whose parameters are specifically generated by the learned latent variables.

reinforcement-learning SMAC+ +1

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