no code implementations • 24 May 2023 • Enrico Picco, Piotr Antonik, Serge Massar
The recognition of human actions in videos is one of the most active research fields in computer vision.
no code implementations • 19 Dec 2020 • Piotr Antonik, Michiel Hermans, Marc Haelterman, Serge Massar
We demonstrate the first opto-electronic reservoir computer with output feedback and test it on two examples of time series generation tasks: frequency and random pattern generation.
no code implementations • 19 Dec 2020 • Piotr Antonik, Marc Haelterman, Serge Massar
We show numerically that online learning allows to circumvent the added complexity of the analogue layer and obtain the same level of performance as with a digital layer.
no code implementations • 6 Apr 2020 • Piotr Antonik, Nicolas Marsal, Damien Rontani
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.
no code implementations • 6 Apr 2020 • Piotr Antonik, Nicolas Marsal, Daniel Brunner, Damien Rontani
We test this approach on a previously reported large-scale experimental system, compare it to the commonly used grid search, and report notable improvements in performance and the number of experimental iterations required to optimise the hyper-parameters.
no code implementations • 6 Apr 2020 • Piotr Antonik, Nicolas Marsal, Daniel Brunner, Damien Rontani
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.
no code implementations • 8 Feb 2018 • Piotr Antonik, Marvyn Gulina, Jaël Pauwels, Serge Massar
We then show that trained reservoir computers can be used to crack chaos based cryptography and illustrate this on a chaos cryptosystem based on the Mackey-Glass system.
no code implementations • 6 Feb 2018 • Piotr Antonik, Marc Haelterman, Serge Massar
Reservoir computing is a bio-inspired computing paradigm for processing time-dependent signals.
no code implementations • 20 Oct 2016 • Piotr Antonik, François Duport, Michiel Hermans, Anteo Smerieri, Marc Haelterman, Serge Massar
Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals.
no code implementations • 20 Oct 2016 • Michiel Hermans, Piotr Antonik, Marc Haelterman, Serge Massar
Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing.