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Sohl-Dickstein and colleagues: learned reverse-diffusion generative models

Contribution grade

Generative-model methods and numerical sampling · Human-designed research method or resource

Reference score220,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Trained a reverse stochastic chain to recover structure after a forward noising process, using a tractable probabilistic training construction.

Scientific scope

Trained a reverse stochastic chain to recover structure after a forward noising process, using a tractable probabilistic training construction. Earlier statistical physics and Markov-chain tools retain credit. Exact sampling from a specified learned chain is not exact recovery of every data distribution or the later DDPM objective.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Earlier statistical physics and Markov-chain tools retain credit. Exact sampling from a specified learned chain is not exact recovery of every data distribution or the later DDPM objective.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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