AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
3 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
MIG-COW is proposed, a novel adversarial attack framework that generates highly generalizable and visually imperceptible adversarial examples that captures both shared and model-specific vulnerabilities across heterogeneous CNN and ViT detectors.
This work presents a taxonomy-guided evaluation protocol for temporal fidelity, in which the applicable measurements are determined by the data rather than fixed in advance, and recasts utility and privacy evaluation over trajectories rather than isolated rows.
Seq2Synth is introduced, a unified benchmark for assessing temporal and schema properties to determine applicable evaluations, covering timestamp, cross-sectional, longitudinal, and structural fidelity, alongside trajectory-aware utility and privacy.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.