Search Results for author: Jonas Wildberger

Found 3 papers, 1 papers with code

Adapting to noise distribution shifts in flow-based gravitational-wave inference

no code implementations16 Nov 2022 Jonas Wildberger, Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

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

no code implementations11 Oct 2022 Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

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

1 code implementation28 Dec 2021 Miroslav Fil, Munib Mesinovic, Matthew Morris, Jonas Wildberger

$\beta$-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.

Disentanglement

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