AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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In recent years, time-series question answering (QA) systems have made significant progress. However, generating a correct answer does not show whether retaining the supplied numerical series improves task performance, nor whether the prediction is sensitive to changes in that input. While some systems provide rational...
Demographic-Independent Fair Graph Learning mitigates group unfairness by minimizing disparities in individual treatment across implicitly identified subgroups, thereby enforcing fairness without requiring explicit demographic information.
A unified objective function for explainable time series learning that bridges attribution and counterfactual reasoning within a single framework is proposed and a novel explanation framework is introduced that learns a parametric transformation network to construct explanation-embedded instances.
IB-Forecast is proposed, an inherently interpretable multivariate time-series forecasting framework that decomposes forecasting into a learned periodic component and a residual component computed with explainable masks over input tokens that guarantees high explanation fidelity.
NoiseFlow is presented, a data-efficient domain adaptation framework that leverages noise-aware modeling and flow matching to enable robust cross-domain generalization and exhibits heterogeneous sensitivity to noise, which can be amplified under domain shift.
Wireless Mesh Networks (WMNs) are essential for many Internet of Things (IoT) applications, such as industrial automation, environmental monitoring, and smart cities. Today, configuring a WMN to meet its stringent performance requirements remains a significant challenge due to dynamic real-world wireless conditions and...
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