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Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control

Yinan Huang et al.
arXiv CS · Papers
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deep-learningmachine-learningneural-networks
Many scientific forecasting tasks require updating a distribution over future trajectories as new observations arrive. Conventional diffusion and flow models generate each forecast from Gaussian noise, often at the cost of many sampling steps. Warm-start methods reuse earlier predictions to reduce t
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