AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 3 of 39 papers
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.
This work proposes Adaptive Parameter Manifold Learning (APML), a novel optimization framework that leverages Local Tangent Space Alignment to discover the underlying manifold structure from training trajectories and optimize neural networks on the parameter manifolds.
Trustworthy security at the network edge demands fine-grained and temporally robust web attack detection to support autonomous threat response under the latency and communication constraints of distributed networked systems. Existing data-driven methods fail to deliver high-performance attack type classification as the...
This work unify these approaches under a Triplet Matrix Decomposition framework and reveals that frequency-domain methods can surpass low-rank approaches when optimal frequency components are selected, and this advantage stems from orthogonal transformation matrices and flexible basis vector combinations.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.