Search Results for author:
Found 5 papers, 3 papers with code
Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions
In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks (GNNs), a domain where deep learning-based approaches have increasingly shown superior performance in both classification and regression tasks compared to traditional methods.
HonestBait: Forward References for Attractive but Faithful Headline Generation
Current methods for generating attractive headlines often learn directly from data, which bases attractiveness on the number of user clicks and views.
Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification
Fine-grained emotion classification (FEC) is a challenging task.
SUVR: A Search-based Approach to Unsupervised Visual Representation Learning
We argue that image pairs should have varying degrees of similarity, and the negative samples should be allowed to be drawn from the entire dataset.
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning
While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage.
Ranked #4 on
Zero-shot Relation Classification
on Wiki-ZSL
