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Conditioning Degenerate Diffusion Models

Paper recorded by Signals 4 on 2026-09-03 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-03 on arXiv · recorded by Signals 4 on 2026-09-04

Category: cs.LG · 机器学习 · first seen 2026-09-04

Abstract

Current conditioned generative models heavily rely on score functions for guidance during training. When the generative model is a diffusion process with a singular diffusion coefficient and the underlying (conditional) densities either do not exist or are not smooth, we use causal optimal transport to define \emph{approximate} loss functions that identify a minimum-entropy control for guidance un

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#134 most recent of 215 cs.LG papers we have recorded · ↑ newer: DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Ho · ↓ older: Subspace Inference Enables Efficient Active Reward Learning from Prefe
Cite this page: Conditioning Degenerate Diffusion Models: the #134 most recent of 215 cs.LG papers we have recorded (as of 2026-09-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/conditioning-degenerate-diffusion-models.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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