Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Nathanaël Perraudin"'
Autor:
Priyanka Chaudhary, João P. Leitão, Tabea Donauer, Stefano D’Aronco, Nathanaël Perraudin, Guillaume Obozinski, Fernando Perez-Cruz, Konrad Schindler, Jan Dirk Wegner, Stefania Russo
Publikováno v:
Water, Vol 14, Iss 19, p 2980 (2022)
We propose a probabilistic deep learning approach for the prediction of maximum water depth hazard maps at high spatial resolutions, which assigns well-calibrated uncertainty estimates to every predicted water depth. Efficient, accurate, and trustwor
Externí odkaz:
https://doaj.org/article/e10d301f4a2b4123b6d7a8b441019a0b
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:
Andreas Loukas, Nathanaël Perraudin
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2019, Iss 1, Pp 1-19 (2019)
Abstract This paper considers regression tasks involving high-dimensional multivariate processes whose structure is dependent on some known graph topology. We put forth a new definition of time-vertex wide-sense stationarity, or joint stationarity fo
Externí odkaz:
https://doaj.org/article/ac9dd9ea137746df826c2e8d504ec888
GIR dataset: A geometry and real impulse response dataset for machine learning research in acoustics
Autor:
Achilleas Xydis, Nathanaël Perraudin, Romana Rust, Kurt Heutschi, Gonzalo Casas, Oksana Riba Grognuz, Kurt Eggenschwiler, Matthias Kohler, Fernando Perez-Cruz
Publikováno v:
Applied Acoustics, 208
Acoustics play a significant role in our everyday lives, influencing our communication, well-being, and perception of space. Fast and precise acoustics simulation is crucial for the accurate design of real spaces by architects and acousticians and ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::777a755a6663b1c6eccbe27a73e133e8
https://hdl.handle.net/20.500.11850/610930
https://hdl.handle.net/20.500.11850/610930
Autor:
Adrien Teurtrie, Nathanaël Perraudin, Thomas Holvoet, Hui Chen, Duncan T.L. Alexander, Guillaume Obozinski, Cécile Hébert
Publikováno v:
Ultramicroscopy, 249
We present two open-source Python packages: “electron spectro-microscopy” (espm) and “electron microscopy tables” (emtables). The espm software enables the simulation of scanning transmission electron microscopy energy-dispersive X-ray spectr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::408f035e78bdbf84243846ea9487fcb3
Autor:
Russo, Priyanka Chaudhary, João P. Leitão, Tabea Donauer, Stefano D’Aronco, Nathanaël Perraudin, Guillaume Obozinski, Fernando Perez-Cruz, Konrad Schindler, Jan Dirk Wegner, Stefania
Publikováno v:
Water; Volume 14; Issue 19; Pages: 2980
We propose a probabilistic deep learning approach for the prediction of maximum water depth hazard maps at high spatial resolutions, which assigns well-calibrated uncertainty estimates to every predicted water depth. Efficient, accurate, and trustwor
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. 15:120-131
In this article, we introduce GACELA, a conditional generative adversarial network (cGAN) designed to restore missing audio data with durations ranging between hundreds of milliseconds and a few seconds, i.e., to perform long-gap audio inpainting. Wh
Autor:
Adrien Teurtrie, Nathanaël Perraudin, Thomas Holvoet, Hui Chen, Duncan T L Alexander, Guillaume Obozinski, Cécile Hébert
Publikováno v:
Microscopy and Microanalysis. 28:2978-2979
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 27:2362-2372
In this article, we study the ability of deep neural networks (DNNs) to restore missing audio content based on its context, i.e., inpaint audio gaps. We focus on a condition which has not received much attention yet: gaps in the range of tens of mill
Autor:
Fabio Gramazio, Matthias Kohler, Romana Rust, Nathanaël Perraudin, Achilleas Xydis, Gonzalo Casas, Kurt Heutschi, Jürgen Strauss, Fernando Perez-Cruz, Chaoyu Du, Kurt Eggenschwiler
In this paper, we present a novel interdisciplinary approach to study the relationship between diffusive surface structures and their acoustic performance. Using computational design, surface structures are iteratively generated and 3D printed at 1:1
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45a37076e41a4f4f84e736203ff507d8
http://arxiv.org/abs/2109.12014
http://arxiv.org/abs/2109.12014