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Found 7 papers, 3 papers with code
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.
Flow Matching for Scalable Simulation-Based Inference
Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging.
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.
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.
On the Interventional Kullback-Leibler Divergence
Modern machine learning approaches excel in static settings where a large amount of i. i. d.
Out-of-Variable Generalization for Discriminative Models
The ability of an agent to do well in new environments is a critical aspect of intelligence.
Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling
Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem.
