Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Antonik, Piotr"'
Publikováno v:
Neural Networks, Volume 165, August 2023, Pages 662-675
The recognition of human actions in videos is one of the most active research fields in computer vision. The canonical approach consists in a more or less complex preprocessing stages of the raw video data, followed by a relatively simple classificat
Externí odkaz:
http://arxiv.org/abs/2305.15283
Publikováno v:
Cognitive Computation (Volume: 9, Pages: 297-306, 11 March 2017)
Introduction. Reservoir Computing is a bio-inspired computing paradigm for processing time-dependent signals. The performance of its hardware implementation is comparable to state-of-the-art digital algorithms on a series of benchmark tasks. The majo
Externí odkaz:
http://arxiv.org/abs/2012.10613
Publikováno v:
Neural Processing Letters (Volume: 47, Pages: 1041-1054, 13 April 2017)
Reservoir computing is a bio-inspired computing paradigm for processing time dependent signals. The performance of its analogue implementations matches other digital algorithms on a series of benchmark tasks. Their potential can be further increased
Externí odkaz:
http://arxiv.org/abs/2012.10615
Publikováno v:
Nat Mach Intell 1, 530-537 (2019)
The recognition of human actions in video streams is a challenging task in computer vision, with cardinal applications in e.g. brain-computer interface and surveillance. Deep learning has shown remarkable results recently, but can be found hard to us
Externí odkaz:
http://arxiv.org/abs/2004.02545
Publikováno v:
IEEE Journal of Selected Topics in Quantum Electronics (Volume: 26 , Issue: 1 , Jan.-Feb. 2020)
We propose a scalable photonic architecture for implementation of feedforward and recurrent neural networks to perform the classification of handwritten digits from the MNIST database. Our experiment exploits off-the-shelf optical and electronic comp
Externí odkaz:
http://arxiv.org/abs/2004.02542
Introduction. Reservoir computing is a growing paradigm for simplified training of recurrent neural networks, with a high potential for hardware implementations. Numerous experiments in optics and electronics yield comparable performance to digital s
Externí odkaz:
http://arxiv.org/abs/2004.02535
Autor:
Antonik, Piotr
Reservoir computing est un ensemble de techniques permettant de simplifierl’utilisation des réseaux de neurones artificiels. Les réalisations expérimentales,notamment optiques, de ce concept ont montré des performances proches de l’étatde l
Publikováno v:
Phys. Rev. E 98, 012215 (2018)
Using the machine learning approach known as reservoir computing, it is possible to train one dynamical system to emulate another. We show that such trained reservoir computers reproduce the properties of the attractor of the chaotic system sufficien
Externí odkaz:
http://arxiv.org/abs/1802.02844
Publikováno v:
Phys. Rev. Applied 7, 054014 -- Published 24 May 2017
Reservoir computing is a bio-inspired computing paradigm for processing time-dependent signals. Its hardware implementations have received much attention because of their simplicity and remarkable performance on a series of benchmark tasks. In previo
Externí odkaz:
http://arxiv.org/abs/1802.02026