arXiv:2607. 17972v1 Announce Type: new Abstract: The prevailing inference framework for diffusion models formulates generation fundamentally as a problem of numerical integration.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 22599v1 Announce Type: new Abstract: Diffusion models have become a widely used framework for probabilistic time series forecasting, modeling the distribution of future values given an observed history.
Probabilistic multivariate time series (MTS) forecasting is crucial for modeling complex dynamical systems. However, existing diffusion-based methods rely on task-specific conditional paradigms that l...
arXiv:2608. 19504v1 Announce Type: new Abstract: We propose a conditioning mechanism for diffusion models based on multi-speed joint diffusion of the target and the condition.
arXiv:2607. 19919v1 Announce Type: cross Abstract: We propose Diffusion ReRoll, a diffusion-based framework for robotic sequential prediction that enables revisable denoising over horizons.
DynG-Diff is a new diffusion-based framework for probabilistic multivariate time‑series forecasting that addresses the challenge of information heterogeneity across variables. It uses a two‑stage training strategy with an unconditional diffusion backbone and introduces a lightweight state‑aware policy network that dynamically adjusts guidance strength based on real‑time variable reliability. The dynamic guidance is mathematically framed as local precision, allowing the model to focus on high‑confidence variables and suppress anomalous noise, leading to competitive performance and robustness on real‑world benchmarks.
Diffusion models have shown strong potential for multi-modal planning in end-to-end autonomous driving. However, most existing methods confine diffusion to the planning module, conditioning on fixed outputs from separate discriminative perception networks.