AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
SIAGXC, a prediction-augmented graph framework for XMLC that leverages auxiliary relational signals derived from an upstream XMLC model, demonstrates that prediction-derived auxiliary relations provide an effective way to enhance graph-based XMLC.
Multi-label classification (MLC) requires predicting multiple relevant labels for each instance, where a central challenge is modeling complex label dependencies arising from co-occurrence patterns. Existing approaches are limited in capturing high-order label correlations, relying on implicit learning through contrast...
By bridging the representation and prediction spaces, MOLN effectively addresses the label-co-occurrence problem, achieving state-of-the-art performance on benchmark datasets with significant improvements.
Extreme multi-label classification (XML) and hierarchical text classification (HTC) address closely related multi-label prediction problems, but have largely developed as separate research areas. XML focuses on very large label spaces and typically evaluates ranked label lists, while HTC assumes a human-curated label h...
This study innovatively analyzes the hyperparameters of tree-based linear methods and suggests an efficient and effective guideline that leads to consistent improvements across datasets, thereby strengthening tree-based linear methods as a stronger XMTC baseline.
The proposed LLM-augmented Multi-Graph Contrastive Learning (LLM-MGCL) is a multi-graph neural network that uses semantic and spatial information about items to extend the LightGCN backbone with two auxiliary item-item graphs that outperforms classical collaborative filtering, matrix factorization, and interaction-only...
Experiments and comparisons show that SEMGNN achieves competitive or improved predictive performance while providing more faithful and compact label-conditioned explanations, compared to post-hoc methods.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.