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Found 4 papers, 2 papers with code
AGS-GNN: Attribute-guided Sampling for Graph Neural Networks
We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs.
GenFighter: A Generative and Evolutive Textual Attack Removal
Adversarial attacks pose significant challenges to deep neural networks (DNNs) such as Transformer models in natural language processing (NLP).
Inferential SIR-GN: Scalable Graph Representation Learning
Many unsupervised node representation learning algorithms are incapable of scaling to large graphs, and are unable to generate node representations for unseen nodes.
V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities
Weaknesses in computer systems such as faults, bugs and errors in the architecture, design or implementation of software provide vulnerabilities that can be exploited by attackers to compromise the security of a system.
Ranked #1 on
General Classification
on CVE to CWE mapping
