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arXiv AI By Jian Xu, Yican Liu, Delu Zeng, John Paisley, Qibin Zhao

Neural Bridge Processes

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Neural Bridge Processes (NBPs) replace the input‑independent forward kernel of Neural Diffusion Processes with an input‑anchored bridge trajectory, allowing conditioning inputs to influence the noisy training states. When input and output dimensions differ, NBPs learn an output‑space anchor that guides the generative path without altering the denoising backbone. Theoretical analysis shows that this anchoring yields pathwise input distinguishability, injects input information into noisy states, and provides a direct gradient pathway, leading to consistent performance gains across synthetic regression, EEG, CylinderFlow, and image regression tasks.

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 AI.

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