Real NVP uses affine coupling layers to create invertible transformations that support exact density estimation, sampling, and latent inference without approximations.
Improving variational inference with inverse autoregressive flow
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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DSEE is a flow-based emulator that generates stellar evolution tracks and isochrones as probabilistic outputs from a single model trained on millions of simulations, enabling fast interpolation and uncertainty-aware analyses.
Generative models including VAEs, normalizing flows, GANs, and diffusion models can learn neutron source distributions from Monte Carlo lists for fast, memory-free sampling after training.
citing papers explorer
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Density estimation using Real NVP
Real NVP uses affine coupling layers to create invertible transformations that support exact density estimation, sampling, and latent inference without approximations.
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Dartmouth Stellar Evolution Emulator (DSEE) 1: Generative Stellar Evolution Model Database
DSEE is a flow-based emulator that generates stellar evolution tracks and isochrones as probabilistic outputs from a single model trained on millions of simulations, enabling fast interpolation and uncertainty-aware analyses.
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Machine Learning for neutron source distributions
Generative models including VAEs, normalizing flows, GANs, and diffusion models can learn neutron source distributions from Monte Carlo lists for fast, memory-free sampling after training.