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Found 5 papers, 1 papers with code
Adversarial Attack against Cross-lingual Knowledge Graph Alignment
Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks.
Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing
Theoretical analysis is conducted to derive that the Nemytskii operator is smooth and induces a Frechet differentiable smooth manifold.
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory
Despite achieving remarkable efficiency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks.
Cross-lingual Entity Alignment with Adversarial Kernel Embedding and Adversarial Knowledge Translation
With the guidance of known aligned entities in the context of multiple random walks, an adversarial knowledge translation model is developed to fill and translate masked entities in pairwise random walks from two KGs.
Adversarial Attacks on Deep Graph Matching
Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations.
