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Generic and Trend-aware Curriculum Learning for Relation Extraction
We present a generic and trend-aware curriculum learning approach that effectively integrates textual and structural information in text graphs for relation extraction between entities, which we consider as node pairs in graphs.
Complexity-Guided Curriculum Learning for Text Graphs
We present a curriculum learning approach that builds on existing knowledge about text and graph complexity formalisms for training with text graph data.
Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach
The proposed solution advances existing research in curriculum learning for graph neural networks with the ability to incorporate a fine-grained spectrum of graph difficulty criteria in their training paradigms.
Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks
We present a generic and trend-aware curriculum learning approach for graph neural networks.
