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The paper proposes FreqDiff, a frequency-aware diffusion framework for temporal knowledge graph extrapolation that improves uncertainty modeling and achieves state-of-the-art performance on benchmarks.
RouteTS is a unified forecasting framework that routes time series components between frequency and time domains based on spectral characteristics, improving accuracy and efficiency in handling periodicity and transience.
This paper introduces the Spectral Energy Centroid (SEC) metric to analyze and improve spectral bias in implicit neural representations, demonstrating its utility for hyperparameter selection, signal complexity measurement, and cross-architecture alignment.