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Direct Intermediate Initialization for Tilted Diffusion Samplers

Paper recorded by Signals 4 on 2026-10-05 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-05 on arXiv · recorded by Signals 4 on 2026-10-06

Category: cs.LG · 机器学习 · first seen 2026-10-06

Abstract

Some diffusion posterior samplers construct Gaussian-tilted intermediate distributions along the reverse process. We observe that these targets can be pulled back to clean-space posteriors with weaker conditioning, with samples transported analytically to the corresponding noisy-space target through a Gaussian bridge. For the sequential Monte Carlo (SMC) sampler MCGDiff, the effective observation

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#372 most recent of 762 cs.LG papers we have recorded · ↑ newer: Variance-Optimal Off-Policy Evaluation with Conjunct Effect Modeling · ↓ older: Towards Looped Models Done Right, Part II: Rethinking at Fixed Points
Cite this page: Direct Intermediate Initialization for Tilted Diffusion Samplers: the #372 most recent of 762 cs.LG papers we have recorded (as of 2026-10-05). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/direct-intermediate-initialization-for-tilted-diffusion-samplers.html
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