Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Exarchakis A"'
Autor:
Ramesh, Sanat, Srivastav, Vinkle, Alapatt, Deepak, Yu, Tong, Murali, Aditya, Sestini, Luca, Nwoye, Chinedu Innocent, Hamoud, Idris, Sharma, Saurav, Fleurentin, Antoine, Exarchakis, Georgios, Karargyris, Alexandros, Padoy, Nicolas
The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amount
Externí odkaz:
http://arxiv.org/abs/2207.00449
We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters. Our algorithm achieves high-performance by evaluating distances of datapoints with a subset of the cluster centres. Our contribution is sub
Externí odkaz:
http://arxiv.org/abs/2112.14793
Autor:
Lawrence J. Mulligan, Julian Thrash, Ludmil Mitrev, Douglas Folk, Alyssa Exarchakis, Daniel Ewert, Jeffrey C. Hill
Publikováno v:
Frontiers in Cardiovascular Medicine, Vol 11 (2024)
IntroductionThis study evaluated the hypothesis that vascular aging (VA) reduces ventricular contractile function and mechanical efficiency (ME) using the left ventricular pressure-volume (PV) construct.MethodsA previously published in-silico computa
Externí odkaz:
https://doaj.org/article/d334352faaca4cee8c98450c95fd1b07
Autor:
Konstantinos Skianis, Giannis Nikolentzos, Benoit Gallix, Rodolphe Thiebaut, Georgios Exarchakis
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023)
Abstract The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regardi
Externí odkaz:
https://doaj.org/article/74acc339434049e48aea565c7a4ab35b
Autor:
Lodi, Massimo, Poterie, Audrey, Exarchakis, Georgios, Brien, Camille, Lafaye de Micheaux, Pierre, Deruelle, Philippe, Gallix, Benoît
Publikováno v:
In Journal of Gynecology Obstetrics and Human Reproduction September 2023 52(7)
Autor:
Ramesh, Sanat, Srivastav, Vinkle, Alapatt, Deepak, Yu, Tong, Murali, Aditya, Sestini, Luca, Nwoye, Chinedu Innocent, Hamoud, Idris, Sharma, Saurav, Fleurentin, Antoine, Exarchakis, Georgios, Karargyris, Alexandros, Padoy, Nicolas
Publikováno v:
In Medical Image Analysis August 2023 88
Autor:
Exarchakis, Georgios, Bornschein, Jörg, Sheikh, Abdul-Saboor, Dai, Zhenwen, Henniges, Marc, Drefs, Jakob, Lücke, Jörg
ProSper is a python library containing probabilistic algorithms to learn dictionaries. Given a set of data points, the implemented algorithms seek to learn the elementary components that have generated the data. The library widens the scope of dictio
Externí odkaz:
http://arxiv.org/abs/1908.06843
Autor:
Andreux, Mathieu, Angles, Tomás, Exarchakis, Georgios, Leonarduzzi, Roberto, Rochette, Gaspar, Thiry, Louis, Zarka, John, Mallat, Stéphane, andén, Joakim, Belilovsky, Eugene, Bruna, Joan, Lostanlen, Vincent, Chaudhary, Muawiz, Hirn, Matthew J., Oyallon, Edouard, Zhang, Sixin, Cella, Carmine, Eickenberg, Michael
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scatt
Externí odkaz:
http://arxiv.org/abs/1812.11214
Publikováno v:
J. Chem. Phys. 148, 241732 (2018)
We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory. Using Gaussian-type orbital functions, we create surrogate electronic densities of the molecule from which we compute
Externí odkaz:
http://arxiv.org/abs/1805.00571
We investigate the optimization of two probabilistic generative models with binary latent variables using a novel variational EM approach. The approach distinguishes itself from previous variational approaches by using latent states as variational pa
Externí odkaz:
http://arxiv.org/abs/1712.08104