AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A model-based measure is introduced that compares a channel-dependent (CD) model to its channel-independent (CI) variant and only lagged MI and the CD gain recover every planted coupling, a model-based measure that compares a channel-dependent model to its channel-independent variant.
Two-stage Odd Residual Flows (TORF), a framework that decouples mean forecasting from uncertainty estimation, achieves state-of-the-art deterministic accuracy (NMAE) while providing strong density estimation performance (CRPS) on short and long-horizon forecasting.
A simple procedure is described that unfolds the joint input-target sequence into overlapping sliding windows, randomly reorders a controlled fraction of them, and reconstructs the sequence by averaging across the overlaps, producing synthetic samples with controlled variation while limiting temporal distortion.
This work proposes asking a different question: could a model's predictions result from any joint distribution?
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