AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We study the problem of learning accurate and cognitively simple binary classifiers that are directly interpretable as Boolean rules. We propose mixed integer linear programming (MILP) formulations for training sparse disjunctive normal form (DNF) networks, viewed as small networks with a conjunctive hidden layer and a...
As interest in explainable artificial intelligence (XAI) continues to grow, a critical gap remains between the explanations generated by models and their interpretability by human users, particularly when cognitive biases and semantic relevance are not adequately addressed. This paper introduces CoRIfEE-Rel, a novel hu...
This paper formalizes a generalizable generate-filter framework and introduces LeDQeR, an LLM-based DQ rule generation approach that is able to produce effective and compact rule sets for various datasets and error types.
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