Physics-constrained generative sampling for PDE inverse problems omits a co-area Jacobian factor required for correct posterior sampling; CoCoS corrects it to match the true distribution.
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Conflict-Aware Additive Guidance (g^car) is a lightweight learnable method that dynamically resolves gradient conflicts to prevent off-manifold drift in compositional guided sampling for flow models.
Exploration of pre-generation prediction of human preference metrics (HPM) from noise seeds in diffusion models to improve output quality with negligible added cost.
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The Right Measure for Physics-Constrained Generation: A Co-Area Correction for Posterior-Consistent PDE Inverse Problems
Physics-constrained generative sampling for PDE inverse problems omits a co-area Jacobian factor required for correct posterior sampling; CoCoS corrects it to match the true distribution.
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Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
Conflict-Aware Additive Guidance (g^car) is a lightweight learnable method that dynamically resolves gradient conflicts to prevent off-manifold drift in compositional guided sampling for flow models.
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Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So?
Exploration of pre-generation prediction of human preference metrics (HPM) from noise seeds in diffusion models to improve output quality with negligible added cost.