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Found 3 papers, 1 papers with code
Adapting to noise distribution shifts in flow-based gravitational-wave inference
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accuracy.
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
This shows a median sample efficiency of $\approx 10\%$ (two orders-of-magnitude better than standard samplers) as well as a ten-fold reduction in the statistical uncertainty in the log evidence.
Beta-VAE Reproducibility: Challenges and Extensions
$\beta$-VAE is a follow-up technique to variational autoencoders that proposes special weighting of the KL divergence term in the VAE loss to obtain disentangled representations.
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