Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Aurelien Lucchi"'
Publikováno v:
Frontiers in Artificial Intelligence, Vol 4 (2021)
Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the Universe and our ability to constrain cosmological models. The prediction of these mass maps is based on expensive N-body simulations, which
Externí odkaz:
https://doaj.org/article/091157675c4c4244a43f10563a185ac7
Autor:
Aurelien Lucchi, Jonas Kohler
Publikováno v:
IMA Journal of Numerical Analysis.
A significant theoretical advantage of high-order optimization methods is their superior convergence guarantees. For instance, third-order regularized methods reach an $(\epsilon _1,\epsilon _2,\epsilon _3)$third-order critical point in at most ${\ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::061fdcf299836a8ed7767b7b3f3c8b28
https://doi.org/10.1093/imanum/drac057
https://doi.org/10.1093/imanum/drac057
Autor:
Janis Fluri, Tomasz Kacprzak, Aurelien Lucchi, Aurel Schneider, Alexandre Refregier, Thomas Hofmann
Publikováno v:
Physical Review D. 105
We present a full forward-modeled $w$CDM analysis of the KiDS-1000 weak lensing maps using graph-convolutional neural networks (GCNN). Utilizing the $\texttt{CosmoGrid}$, a novel massive simulation suite spanning six different cosmological parameters
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b05036a6ffd1ea85e4bcc2ff87ffc309
https://doi.org/10.1103/physrevd.105.083518
https://doi.org/10.1103/physrevd.105.083518
The $d$-dimensional Ornstein--Uhlenbeck process (OUP) describes the trajectory of a particle in a $d$-dimensional, spherically symmetric, quadratic potential. The OUP is composed of a drift term weighted by a constant $\theta \geq 0$ and a diffusion
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f13f6b26c66679d8e30456f6efe80ab3
Publikováno v:
Scopus-Elsevier
We study the theoretical convergence properties of random-search methods when optimizing non-convex objective functions without having access to derivatives. We prove that standard random-search methods that do not rely on second-order information co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b6fc8ca5e31171b5ae489246f905e98
http://arxiv.org/abs/2110.13265
http://arxiv.org/abs/2110.13265
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Recent advances in differentiable rendering have sparked an interest in learning generative models of textured 3D meshes from image collections. These models natively disentangle pose and appearance, enable downstream applications in computer graphic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5ebc787b3cb23314f55be12dc6a8cee
https://doi.org/10.1109/iccv48922.2021.01362
https://doi.org/10.1109/iccv48922.2021.01362
The ability to obtain reliable point estimates of model parameters is of crucial importance in many fields of physics. This is often a difficult task given that the observed data can have a very high number of dimensions. In order to address this pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8995257ca87c2f4cfc15e0c6a43bde63
Publikováno v:
NeurIPS 2020-Thirty-fourth Conference on Neural Information Processing Systems
NeurIPS 2020-Thirty-fourth Conference on Neural Information Processing Systems, Dec 2020, Virtual, France
Scopus-Elsevier
NeurIPS 2020-Thirty-fourth Conference on Neural Information Processing Systems, Dec 2020, Virtual, France
Scopus-Elsevier
International audience; Randomly initialized neural networks are known to become harder to train with increasing depth, unless architectural enhancements like residual connections and batch normalization are used. We here investigate this phenomenon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d4c2c3d95b7e4ec665efe70869488a6
https://hal.science/hal-03454386/document
https://hal.science/hal-03454386/document
Fault-tolerant quantum computers offer the promise of dramatically improving machine learning through speed-ups in computation or improved model scalability. In the near-term, however, the benefits of quantum machine learning are not so clear. Unders
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a561a3177ef74d17c619f8b150af18de
http://arxiv.org/abs/2011.00027
http://arxiv.org/abs/2011.00027
Publikováno v:
Scopus-Elsevier
Derivative-free optimization (DFO) has recently gained a lot of momentum in machine learning, spawning interest in the community to design faster methods for problems where gradients are not accessible. While some attention has been given to the conc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94560801f2b69cdbd1e6dd5cc69099b9
http://arxiv.org/abs/2007.03311
http://arxiv.org/abs/2007.03311