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Found 4 papers, 0 papers with code
Multi-task Envisioning Transformer-based Autoencoder for Corporate Credit Rating Migration Early Prediction
META consists of Positional Encoding, Transformer-based Autoencoder, and Multi-task Prediction to learn effective representations for both migration prediction and rating prediction.
Label-invariant Augmentation for Semi-Supervised Graph Classification
Recently, contrastiveness-based augmentation surges a new climax in the computer vision domain, where some operations, including rotation, crop, and flip, combined with dedicated algorithms, dramatically increase the model generalization and robustness.
Second-Order Unsupervised Feature Selection via Knowledge Contrastive Distillation
Unsupervised feature selection aims to select a subset from the original features that are most useful for the downstream tasks without external guidance information.
Graph-Graph Similarity Network
In this paper, we propose a Graph-Graph Similarity Network to tackle the graph classification problem by constructing a SuperGraph through learning the relationships among graphs.
