Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Makarenko, Maksim"'
Autor:
Makarenko, Maksim
The development of modern Machine Learning (ML) frameworks trained on large datasets established a rapid increase in the performance of cognitive computing algorithms for a wide range of applications. However, due to the processing capacity restricti
Externí odkaz:
http://hdl.handle.net/10754/686004
Autor:
Makarenko, Maksim, Wang, Qizhou, Burguete-Lopez, Arturo, Giancola, Silvio, Ghanem, Bernard, Passone, Luca, Fratalocchi, Andrea
Foundation models, exemplified by GPT technology, are discovering new horizons in artificial intelligence by executing tasks beyond their designers' expectations. While the present generation provides fundamental advances in understanding language an
Externí odkaz:
http://arxiv.org/abs/2312.10639
Autor:
Barkey, Martin, Büchner, Rebecca, Wester, Alwin, Pritzl, Stefanie D., Makarenko, Maksim, Wang, Qizhou, Weber, Thomas, Trauner, Dirk, Maier, Stefan A., Fratalocchi, Andrea, Lohmüller, Theobald, Tittl, Andreas
Nanophotonic devices excel at confining light into intense hot spots of the electromagnetic near fields, creating unprecedented opportunities for light-matter coupling and surface-enhanced sensing. Recently, all-dielectric metasurfaces with ultrashar
Externí odkaz:
http://arxiv.org/abs/2308.15644
We propose Adaptive Compressed Gradient Descent (AdaCGD) - a novel optimization algorithm for communication-efficient training of supervised machine learning models with adaptive compression level. Our approach is inspired by the recently proposed th
Externí odkaz:
http://arxiv.org/abs/2211.00188
Autor:
Makarenko, Maksim, Burguete-Lopez, Arturo, Wang, Qizhou, Getman, Fedor, Giancola, Silvio, Ghanem, Bernard, Fratalocchi, Andrea
Hyperspectral imaging has attracted significant attention to identify spectral signatures for image classification and automated pattern recognition in computer vision. State-of-the-art implementations of snapshot hyperspectral imaging rely on bulky,
Externí odkaz:
http://arxiv.org/abs/2204.02084
Autor:
Wang, Qizhou, Makarenko, Maksim
This work introduced a novel GAN architecture for unsupervised image translation on the task of face style transform. A spectral attention-based mechanism is embedded into the design along with spatial attention on the image contents. We proved that
Externí odkaz:
http://arxiv.org/abs/2110.02873
Publikováno v:
In Gene 20 December 2023 888
Autor:
Burguete-Lopez, Arturo1 (AUTHOR), Makarenko, Maksim1 (AUTHOR), Bonifazi, Marcella1,2 (AUTHOR), Menezes de Oliveira, Barbara Nicoly1 (AUTHOR), Getman, Fedor1 (AUTHOR), Tian, Yi1 (AUTHOR), Mazzone, Valerio1,2 (AUTHOR), Li, Ning1 (AUTHOR), Giammona, Alessandro3,4 (AUTHOR), Liberale, Carlo3 (AUTHOR), Fratalocchi, Andrea1 (AUTHOR) andrea.fratalocchi@kaust.edu.sa
Publikováno v:
Communications Biology. 2/6/2024, Vol. 7 Issue 1, p1-9. 9p.
Integrating conventional optics into compact nanostructured surfaces is the goal of flat optics. Despite the enormous progress of this technology, there are still critical challenges for real world applications due to a limited efficiency in the visi
Externí odkaz:
http://arxiv.org/abs/2005.01954
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