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pro vyhledávání: '"Wildberger, Jonas"'
Autor:
Dax, Maximilian, Green, Stephen R., Gair, Jonathan, Gupte, Nihar, Pürrer, Michael, Raymond, Vivien, Wildberger, Jonas, Macke, Jakob H., Buonanno, Alessandra, Schölkopf, Bernhard
Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817 led to scientific discoveries across cosmology, nuclear physics, and
Externí odkaz:
http://arxiv.org/abs/2407.09602
Autor:
Gupte, Nihar, Ramos-Buades, Antoni, Buonanno, Alessandra, Gair, Jonathan, Miller, M. Coleman, Dax, Maximilian, Green, Stephen R., Pürrer, Michael, Wildberger, Jonas, Macke, Jakob, Romero-Shaw, Isobel M., Schölkopf, Bernhard
Binary black holes (BBHs) in eccentric orbits produce distinct modulations the emitted gravitational waves (GWs). The measurement of orbital eccentricity can provide robust evidence for dynamical binary formation channels. We analyze 57 GW events fro
Externí odkaz:
http://arxiv.org/abs/2404.14286
Autor:
Gebhard, Timothy D., Wildberger, Jonas, Dax, Maximilian, Angerhausen, Daniel, Quanz, Sascha P., Schölkopf, Bernhard
Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem. However, traditional approaches such as nested sampling are computati
Externí odkaz:
http://arxiv.org/abs/2312.08295
Autor:
Dax, Maximilian, Wildberger, Jonas, Buchholz, Simon, Green, Stephen R., Macke, Jakob H., Schölkopf, Bernhard
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. Building on recent advances in generative mo
Externí odkaz:
http://arxiv.org/abs/2305.17161
The ability of an agent to do well in new environments is a critical aspect of intelligence. In machine learning, this ability is known as $\textit{strong}$ or $\textit{out-of-distribution}$ generalization. However, merely considering differences in
Externí odkaz:
http://arxiv.org/abs/2304.07896
Modern machine learning approaches excel in static settings where a large amount of i.i.d. training data are available for a given task. In a dynamic environment, though, an intelligent agent needs to be able to transfer knowledge and re-use learned
Externí odkaz:
http://arxiv.org/abs/2302.05380
Autor:
Wildberger, Jonas, Dax, Maximilian, Green, Stephen R., Gair, Jonathan, Pürrer, Michael, Macke, Jakob H., Buonanno, Alessandra, Schölkopf, Bernhard
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accuracy. These approaches (e.g., DINGO) enable amortized inference by trai
Externí odkaz:
http://arxiv.org/abs/2211.08801
Autor:
Dax, Maximilian, Green, Stephen R., Gair, Jonathan, Pürrer, Michael, Wildberger, Jonas, Macke, Jakob H., Buonanno, Alessandra, Schölkopf, Bernhard
Publikováno v:
Phys. Rev. Lett. 130, 171403 (2023)
We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights base
Externí odkaz:
http://arxiv.org/abs/2210.05686
$\beta$-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. Unsupervised learning is known to be brittle even on toy datasets and a
Externí odkaz:
http://arxiv.org/abs/2112.14278
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