The method couples Bayesian spectral deconvolution with a Gaussian process physical-property regression layer to select peak models consistent with auxiliary measurements, recovering meaningful structures missed by spectrum-only inference.
Latuszy ´nski, M
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Energy-Weighted Flow Matching reformulates conditional flow matching with importance sampling to enable continuous normalizing flows to model Boltzmann distributions from energy evaluations alone, with iterative and annealed variants showing competitive performance on benchmarks.
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Integrating Bayesian Spectral Deconvolution and Expert Scientific Reasoning for Robust Peak Estimation
The method couples Bayesian spectral deconvolution with a Gaussian process physical-property regression layer to select peak models consistent with auxiliary measurements, recovering meaningful structures missed by spectrum-only inference.
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Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling
Energy-Weighted Flow Matching reformulates conditional flow matching with importance sampling to enable continuous normalizing flows to model Boltzmann distributions from energy evaluations alone, with iterative and annealed variants showing competitive performance on benchmarks.