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Found 7 papers, 4 papers with code
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration
Second, we introduce a heterogeneous layer to be inserted into decision-making networks, whose parameters are specifically generated by the learned latent variables.
STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction
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.
Embedding Decomposition for Artifacts Removal in EEG Signals
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.
Improving Relation Extraction with Relational Paraphrase Sentences
In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
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.
Data-driven Inverter-based Volt/VAr Control for Partially Observable Distribution Networks
To provide a practical Volt/Var control (VVC) strategy for such networks, a data-driven VVC method is proposed in this paper.
