AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results indicate that effective TabFM transfer depends on the data regime and on the statistical structure represented by the chosen adaptation interface, and that classification fine-tuning becomes more competitive with zero-shot inference as data sets grow but remains weaker for probabilistic prediction.
MSPCIFormer is proposed, a novel Transformer‐based architecture that integrates multi‐scale patching with channel‐independent (CI) modelling to capture heterogeneous temporal dynamics while mitigating noise from time‐varying inter‐asset correlations.
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