AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A fuzzy granular-ball random vector functional link (GBFRVFL) framework that leverages granular-ball computing to abstract raw samples into adaptive granular balls and introduces two membership assignment schemes that enhance robustness to noise, outliers, class imbalance, and uncertainty in granular-ball distributions...
It is suggested that generic preconditioning approaches may not adequately capture the optimization characteristics of biomedical tabular learning, motivating the development of biomedical-aware preconditioners specifically tailored for healthcare-oriented tabular foundation models.
Experiments on UCI benchmark datasets validate the superiority of the proposed IFW-BLS model over the baseline models over the baseline models; additional corruption experiments also show more stable performance than BLS under noise and outlier contamination.
The kernel risk-sensitive mean p-power based RVFL (KRPRVFL) model is proposed, which integrates the computational efficiency of RVFL with the robustness of the kernel risk-sensitive mean p-power (KRP) criterion and adaptively reduces the influence of corrupted or unreliable samples during training, resulting in improve...
Wave-BLS, a robust broad learning framework that integrates the wave loss function, which is asymmetric, bounded, and smooth, enabling controlled penalization of large errors, is proposed, establishing Wave-BLS as a stable and robust alternative to existing broad learning models for learning under data uncertainty.
The first complex augmented Broad Learning System (CA-BLS) is introduced, which transforms real-valued inputs into phase-encoded complex representations and adopts widely linear modeling to jointly leverage covariance and pseudo-covariance information via complex conjugate augmentation, enabling effective modeling of l...
GraM-Diff is proposed, a unified classifier-guided Graph-Mamba diffusion framework for EEG synthesis that embeds Graph Convolutional Networks within a diffusion U-Net to model inter-electrode connectivity and Bidirectional Mamba state-space blocks for linear-complexity long-range temporal modeling.
This work proposes a novel physics-informed broad learning system (PI-BLS), the first physics-informed learning framework based on broad RdNNs that achieves competitive and often superior performance with reduced training time and model parameters compared with conventional PINNs.
Extensive experiments conducted on UCI and KEEL benchmark datasets demonstrate the superiority of the proposed IF-dRVFL and IF-edRVFL models over existing SOTA fuzzy and non-fuzzy approaches.
RoBell-RVFL is proposed, a robust and lightweight generalized bell random vector functional link network that redefines how randomized models handle class imbalance and noisy data and achieves adaptive control over sample contributions without sacrificing the closed-form learning efficiency of RVFL networks.
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