Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Michaël Defferrard"'
Autor:
Andreas Scheck, Stéphane Rosset, Michaël Defferrard, Andreas Loukas, Jaume Bonet, Pierre Vandergheynst, Bruno E Correia
Publikováno v:
PLoS Computational Biology, Vol 18, Iss 3, p e1009178 (2022)
Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprin
Externí odkaz:
https://doaj.org/article/2ada425f94364971b53a08385d660ef0
Autor:
Katharina Glomb, Joan Rué Queralt, David Pascucci, Michaël Defferrard, Sébastien Tourbier, Margherita Carboni, Maria Rubega, Serge Vulliémoz, Gijs Plomp, Patric Hagmann
Publikováno v:
NeuroImage, Vol 221, Iss , Pp 117137- (2020)
We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in
Externí odkaz:
https://doaj.org/article/a742bda64cfb44aa9b7349eac622f363
Autor:
Jaume Bonet, Andreas Loukas, Michaël Defferrard, Pierre Vandergheynst, Bruno E. Correia, Stéphane Rosset, Andreas Scheck
Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8dd84f278e2ca3fd8b5b3a415dc95c7a
https://doi.org/10.1101/2021.06.16.448645
https://doi.org/10.1101/2021.06.16.448645
Publikováno v:
Astronomy and Computing, 27
Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. These networks have mostly been developed for regular Euclidean domains such as those supporting images
Ensemble predictions are essential to characterize the forecast uncertainty and the likelihood of an event to occur. Stochasticity in predictions comes from data and model uncertainty. In deep learning (DL), data uncertainty can be approached by trai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e0695df6b9ef543a60441a49e60c3056
https://doi.org/10.5194/egusphere-egu21-2401
https://doi.org/10.5194/egusphere-egu21-2401
Deep Learning (DL) has the potential to revolutionize numerical weather predictions (NWP) and climate simulations by improving model components and reducing computing time, which could then be used to increase the resolution or the number of simulati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::324f460cd5f755c38c4d03c3852e702f
https://doi.org/10.5194/egusphere-egu21-2681
https://doi.org/10.5194/egusphere-egu21-2681
Autor:
Allan Heydon, Benjamin Ricaud, Linjun Shou, Dmitry Ustalov, Yanick Schraner, George Yu, Daria Baidakova, Ly Dinh, Paul Groth, Mehrnoosh Sameki, Marinka Zitnik, Flavian Vasile, Krishnaram Kenthapadi, Benjamin Wollmer, Felix Gessert, Da-Cheng Juan, Hong Cheng, Javier Albert, David Rohde, Onur Celebi, Robert West, Xiang Wang, Dawei Yin, Amine Benhalloum, Junzhou Huang, Fuchun Sun, Michaël Defferrard, Ming Gong, Rezvaneh Rezapour, Levan Tsinadze, Shubhanshu Mishra, Stratis Ioannidis, Francisco M. Couto, Yicheng Fan, Xiangnan He, Christian Scheller, Yueqi Wang, Yu Rong, Pasquale Lisena, Sharada P. Mohanty, Nicolas Aspert, Irene Teinemaa, Chun-Ta Lu, Volodymyr Miz, Jiawei Chen, Johny Jose, Xiangyu Zhao, Philip Pham, Yatao Bian, Manuel K. Schneider, Jennifer G. Dy, Nashlie Sephus, Dmitri Goldenberg, Jiliang Tang, Fuli Feng, Wenbing Huang, Olivier Jeunen, Wenqi Fan, Nikita Popov, Mario Koenig, Shobeir Fakhraei, Olesia Altunina, Smriti Bhagat, Samin Aref, Chun-Sung Ferng, Wolfram Wingerath, Evann Courdier, Martin Müller, Xiubo Geng, Xingjie Zhou, Otmane Sakhi, Dragan Cvetinovic, Florian Laurent, Norbert Ritter, Cesar Ilharco Magalhaes, Stephan Succo, Jian Pei, Ben Packer, Tingyang Xu, Ilkay Yildiz, Rose Howell, Jana Diesner, Tudor Mihai Avram, Arjun Gopalan, Alexey Drutsa, Daxin Jiang, Albert Meroño-Peñuela, Christos Faloutsos
Publikováno v:
The Web Conference 2021: companion of the World Wide Web Conference WWW 2021: April 19-23, 2021, Ljubljana, Slovenia
The Web Conference 2021
30th World Wide Web (WWW) Conference (WebConf), APR 19-23, 2021, ELECTR NETWORK
WEB CONFERENCE 2021: COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2021)
The Web Conference 2021
30th World Wide Web (WWW) Conference (WebConf), APR 19-23, 2021, ELECTR NETWORK
WEB CONFERENCE 2021: COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2021)
This report summarizes the 23 tutorials hosted at The Web Conference 2021: nine lecture-style tutorials and 14 hands-on tutorials.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8db21f1ce6612200bda91f82be15d8cf
https://doi.org/10.1145/3442442.3453701
https://doi.org/10.1145/3442442.3453701
Autor:
Gijs Plomp, Serge Vulliemoz, Michaël Defferrard, Margherita Carboni, Joan Rue Queralt, Katharina Glomb, Sébastien Tourbier, David Pascucci, Patric Hagmann, Maria Rubega
Publikováno v:
NeuroImage, Vol 221, Iss, Pp 117137-(2020)
NeuroImage, Vol. 221 (2020) P. 117137
NeuroImage, Vol. 221 (2020) P. 117137
We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in
Poster presented at the workshop Geometry of Complex Web (Les Diablaretes, February 2-5 2020 https://sites.google.com/view/geocow2020).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27f9869a76cb4eceba601a714ead18f1
Autor:
Michaël Defferrard
Talk at the AI & Networks track of the Applied Machine Learning Conference (AMLD). Video recording at https://youtu.be/mPHPEozOW5Q.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::080ab8b1e808dd038e2fb27c14167844