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Diffusion models adapted for complex inference and control tasks · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new methods for using diffusion models as flexible priors in complex inference tasks. The first paper explores adapting diffusion models as plug-and-play modules for tasks like conditional generation and image segmentation by iterating differentiation through the denoising network. The second paper introduces a data-free learning objective called relative trajectory balance, derived from a generative flow network perspective, to enable amortized sampling of intractable posteriors in diffusion models. This approach has shown promise in vision, language, and control tasks, including achieving state-of-the-art results in offline reinforcement learning. AI

IMPACT These advancements in diffusion models could enable more sophisticated generative AI applications and improve performance in control and inference tasks.

RANK_REASON The cluster contains two academic papers published on arXiv detailing novel research into diffusion models.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Diffusion models adapted for complex inference and control tasks · 2 sources tracked

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23 / 100
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The cluster contains two academic papers published on arXiv detailing novel research into diffusion models.
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2 independent sources
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paper, model release
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Alexandros Graikos, Esmeralda S. Whitammer, Nebojsa Jojic, Dimitris Samaras ·

    Diffusion models as plug-and-play priors

    arXiv:2206.09012v4 Announce Type: replace Abstract: We consider the problem of inferring high-dimensional data $\mathbf{x}$ in a model that consists of a prior $p(\mathbf{x})$ and an auxiliary differentiable constraint $c(\mathbf{x},\mathbf{y})$ on $x$ given some additional infor…

  2. arXiv cs.LG TIER_1 English(EN) · Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yoshua Bengio, Glen Berseth, Esmeralda S. Whitammer ·

    Amortizing intractable inference in diffusion models for vision, language, and control

    arXiv:2405.20971v3 Announce Type: replace Abstract: Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem. This paper studies…

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