Normalizing flows are constructed by learning the velocity of a stochastic interpolant via a quadratic loss derived from its probability current, yielding an efficient ODE-based alternative to diffusion models.
Advances in Neural Information Processing Systems , editor=
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JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
SED modifies diffusion models to generate only non-zero values in sparse data, preserving sparsity patterns, cutting computation, and matching or beating standard DM performance on benchmarks.
citing papers explorer
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Building Normalizing Flows with Stochastic Interpolants
Normalizing flows are constructed by learning the velocity of a stochastic interpolant via a quadratic loss derived from its probability current, yielding an efficient ODE-based alternative to diffusion models.
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Let EEG Models Learn EEG
JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
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Skipping the Zeros in Diffusion Models for Sparse Data Generation
SED modifies diffusion models to generate only non-zero values in sparse data, preserving sparsity patterns, cutting computation, and matching or beating standard DM performance on benchmarks.