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Paper details mathematical principles of generative modeling techniques

By PulseAugur Editorial · [3 sources] ·

This paper explores the mathematical underpinnings of generative modeling, drawing connections between optimal transport and techniques like Schrödinger bridge and flow matching. It aims to provide a high-level overview of these principles for those interested in the field. AI

RANK_REASON The item is an arXiv preprint detailing mathematical principles of generative modeling. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Paper details mathematical principles of generative modeling techniques

COVERAGE [3]

  1. arXiv stat.ML TIER_1 English(EN) · Titouan Vayer (COMPACT) ·

    Notes on generative modeling: flow matching, diffusion, optimal transport and Schr{\"o}dinger bridge

    arXiv:2606.30053v1 Announce Type: new Abstract: These notes recapitulate the high level mathematical principles behind different techniques for generative modeling. I show the connections between optimal transport and standard techniques such as Schr{\"o}dinger bridge and flow ma…

  2. arXiv stat.ML TIER_1 English(EN) · Ziyu Chen, Markos A. Katsoulakis, Benjamin J. Zhang ·

    Robustness and Structure Preservation in Flow-Based Generative Models via Wasserstein Path-Space Divergences

    arXiv:2410.01244v2 Announce Type: replace Abstract: We introduce a novel Wasserstein-1 ($W_1$) path-space divergence for stochastic and deterministic dynamics and establish a Wasserstein Uncertainty Propagation (WUP) theorem that bounds the $W_1$ distance between terminal distrib…

  3. arXiv stat.ML TIER_1 English(EN) · Titouan Vayer ·

    Notes on generative modeling: flow matching, diffusion, optimal transport and Schr{ö}dinger bridge

    These notes recapitulate the high level mathematical principles behind different techniques for generative modeling. I show the connections between optimal transport and standard techniques such as Schr{ö}dinger bridge and flow matching.

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