Wavelet Methods
Overview
Wavelet methods decompose signals into coarse trends and localized details, making them useful when important patterns vary in scale or occur briefly in time or space. Common approaches include discrete and continuous wavelet transforms, scattering features, and adaptive or lifting-based decompositions, often combined with neural networks for forecasting, image and audio analysis, and partial differential equations. Current work explores trainable wavelet bases and their use in transformers, neural operators, and generative models, alongside efficient implementations and new filter designs. A central tension is how to balance localization, stability, and information preservation against transform cost and the need for broad receptive fields; the best basis and decomposition depth can depend strongly on the task.
Latest papers
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