Research extending tabular denoising diffusion probabilistic models to time-series generation. Contributes generative modeling techniques for sequential and temporal data synthesis.
Research
Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation
Denoising diffusion models extended to time-series generation, enabling synthetic temporal data that preserves sequential patterns and temporal dependencies.
Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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