no code implementations • 13 Dec 2023 • Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf
Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem.
1 code implementation • NeurIPS 2023 • Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf
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.
no code implementations • 16 Apr 2023 • Siyuan Guo, Jonas Wildberger, Bernhard Schölkopf
The ability of an agent to do well in new environments is a critical aspect of intelligence.
no code implementations • 10 Feb 2023 • Jonas Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf
Modern machine learning approaches excel in static settings where a large amount of i. i. d.
no code implementations • 16 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.
1 code implementation • 11 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.
1 code implementation • 28 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.