3 accepted papers
Text-attributed graphs (TAGs), which associate rich textual descriptions with each node, are widely employed to represent complex relationships among real-world textual entities. Currently, representation learning for TAGs leverages large language models (LLMs) to transform node-matched textual desc
Fine-tuning Pre-trained Language Models (PLMs) is a popular Natural Language Processing (NLP) paradigm for addressing Named Entity Recognition (NER) tasks. However, neural network models often demonstrate poor generalization capabilities due to significant disparities between the knowledge learned b…
In recent years, a number of lightweight single-image super-resolution (SISR) network methods heave been proposed. However, most existing approaches do not make full use of the information before and after the convolution and the high-frequency information of the image. In this paper, we propose a l…