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Non-denoising forward-time diffusions

7 Pith papers cite this work. Polarity classification is still indexing.

7 Pith papers citing it

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Variational Optimality of F\"ollmer Processes in Generative Diffusions

math.ST · 2026-02-11 · unverdicted · novelty 8.0

Föllmer processes are variationally optimal among generative diffusions because they minimize the impact of drift estimation error on path-space KL divergence, rendering different interpolation schedules statistically equivalent.

Aligning Flow Map Policies with Optimal Q-Guidance

cs.LG · 2026-05-12 · unverdicted · novelty 7.0

Flow map policies enable fast one-step inference for flow-based RL policies, and FMQ provides an optimal closed-form Q-guided target for offline-to-online adaptation under trust-region constraints, achieving SOTA performance.

Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions

cs.LG · 2026-05-09 · unverdicted · novelty 6.0

Discrete flow matching on Z_m^d achieves non-asymptotic KL bounds for early-stopped targets and explicit TV convergence to the true target under an approximation error assumption, with improved scaling in dimension d and vocabulary size m.

Flow Matching Guide and Code

cs.LG · 2024-12-09 · unverdicted · novelty 2.0

Flow Matching is a generative modeling framework with mathematical foundations, design choices, extensions, and open-source PyTorch code for applications like image and text generation.

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  • Variational Optimality of F\"ollmer Processes in Generative Diffusions math.ST · 2026-02-11 · unverdicted · none · ref 35

    Föllmer processes are variationally optimal among generative diffusions because they minimize the impact of drift estimation error on path-space KL divergence, rendering different interpolation schedules statistically equivalent.