Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Carron Igor"'
Optical training of large-scale Transformers and deep neural networks with direct feedback alignment
Autor:
Wang, Ziao, Müller, Kilian, Filipovich, Matthew, Launay, Julien, Ohana, Ruben, Pariente, Gustave, Mokaadi, Safa, Brossollet, Charles, Moreau, Fabien, Cappelli, Alessandro, Poli, Iacopo, Carron, Igor, Daudet, Laurent, Krzakala, Florent, Gigan, Sylvain
Modern machine learning relies nearly exclusively on dedicated electronic hardware accelerators. Photonic approaches, with low consumption and high operation speed, are increasingly considered for inference but, to date, remain mostly limited to rela
Externí odkaz:
http://arxiv.org/abs/2409.12965
Publikováno v:
EPJ Web of Conferences, Vol 106, p 07002 (2016)
A new approach to neutron detection capable of gathering spectroscopic information has been demonstrated. The approach relies on an asymmetrical arrangement of materials, geometry, and an ability to change the orientation of the detector with respect
Externí odkaz:
https://doaj.org/article/73c195f609d146bf9c59b372bb5354d9
Autor:
Brossollet, Charles, Cappelli, Alessandro, Carron, Igor, Chaintoutis, Charidimos, Chatelain, Amélie, Daudet, Laurent, Gigan, Sylvain, Hesslow, Daniel, Krzakala, Florent, Launay, Julien, Mokaadi, Safa, Moreau, Fabien, Müller, Kilian, Ohana, Ruben, Pariente, Gustave, Poli, Iacopo, Tommasone, Elena
We introduce LightOn's Optical Processing Unit (OPU), the first photonic AI accelerator chip available on the market for at-scale Non von Neumann computations, reaching 1500 TeraOPS. It relies on a combination of free-space optics with off-the-shelf
Externí odkaz:
http://arxiv.org/abs/2107.11814
Autor:
Hesslow, Daniel, Cappelli, Alessandro, Carron, Igor, Daudet, Laurent, Lafargue, Raphaël, Müller, Kilian, Ohana, Ruben, Pariente, Gustave, Poli, Iacopo
Randomized Numerical Linear Algebra (RandNLA) is a powerful class of methods, widely used in High Performance Computing (HPC). RandNLA provides approximate solutions to linear algebra functions applied to large signals, at reduced computational costs
Externí odkaz:
http://arxiv.org/abs/2104.14429
Autor:
Launay, Julien, Poli, Iacopo, Müller, Kilian, Pariente, Gustave, Carron, Igor, Daudet, Laurent, Krzakala, Florent, Gigan, Sylvain
The scaling hypothesis motivates the expansion of models past trillions of parameters as a path towards better performance. Recent significant developments, such as GPT-3, have been driven by this conjecture. However, as models scale-up, training the
Externí odkaz:
http://arxiv.org/abs/2012.06373
Autor:
Launay, Julien, Poli, Iacopo, Müller, Kilian, Carron, Igor, Daudet, Laurent, Krzakala, Florent, Gigan, Sylvain
As neural networks grow larger and more complex and data-hungry, training costs are skyrocketing. Especially when lifelong learning is necessary, such as in recommender systems or self-driving cars, this might soon become unsustainable. In this study
Externí odkaz:
http://arxiv.org/abs/2006.01475
Autor:
Saade, Alaa, Caltagirone, Francesco, Carron, Igor, Daudet, Laurent, Drémeau, Angélique, Gigan, Sylvain, Krzakala, Florent
Publikováno v:
Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pages: 6215 - 6219
Random projections have proven extremely useful in many signal processing and machine learning applications. However, they often require either to store a very large random matrix, or to use a different, structured matrix to reduce the computational
Externí odkaz:
http://arxiv.org/abs/1510.06664
Autor:
Liutkus, Antoine, Martina, David, Popoff, Sébastien, Chardon, Gilles, Katz, Ori, Lerosey, Geoffroy, Gigan, Sylvain, Daudet, Laurent, Carron, Igor
Publikováno v:
Sci. Rep. 4, 5552 (2014)
The recent theory of compressive sensing leverages upon the structure of signals to acquire them with much fewer measurements than was previously thought necessary, and certainly well below the traditional Nyquist-Shannon sampling rate. However, most
Externí odkaz:
http://arxiv.org/abs/1309.0425
Autor:
Gigan, Sylvain, Krzakala, Florent, Daudet, Laurent, Carron, Igor, Gigan, Sylvain, Krzakala, Florent, Daudet, Laurent, Carron, Igor
Publikováno v:
Photoniques; September 2020, Vol. 2020 Issue: 104 p49-52, 4p
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