Reservoir computers (RCs) are randomized recurrent neural networks well adapted to process time series, performing tasks such as nonlinear distortion compensation or prediction of chaotic dynamics. Deep reservoir computers (deep-RCs), in which the output of one reservoir is used as the input for another one, can lead to improved performance because, as in other deep artificial neural networks, the successive layers represent the data in more and more abstract ways. We present a fiber-based photonic implementation of a two-layer deep-RC based on frequency multiplexing. The two RC layers are encoded in two frequency combs propagating in the same experimental setup. The connection between the layers is fully analog and does not require any dig...
Als uitgangspunt fungeerde de vraag 'hoe op basis van toegekende rechtsaanspraken tot een doelmatige...
International audiencePhotonic delay systems have revolutionized the hardware implementation of Recu...
International audienceAn efficient photonic hardware integration of neural networks can benefit us f...
Reservoir computing is a brain-inspired approach for information processing, well suited to analog i...
Deep neural networks usually process information through multiple hidden layers. However, most hardw...
Machine Learning (ML) approaches like Deep Neural Networks (DNNs) have emerged as a powerful tool fo...
For many challenging problems where the mathematical description is not explicitly defined, artifici...
International audienceWe review a novel paradigm that has emerged in analogue neuromorphic optical c...
Driven by the remarkable breakthroughs during the past decade, photonics neural networks have experi...
Reservoir computing is a recent bio-inspired approach for processing time-dependent signals. It has ...
Reservoir Computing [1] is a new approach to study and use Neural Networks, which try to mimic a bra...
Reservoir computing (RC) is a technique in machine learning inspired by neural systems. RC has been ...
We study numerically a realistic model of an original autonomous implementation of a photonic neural...
In this thesis we study photonic computation within the framework of reservoir computing. Inspired b...
We present an autonomous all-photonic experimental implementation of an artificial neural network ba...
Als uitgangspunt fungeerde de vraag 'hoe op basis van toegekende rechtsaanspraken tot een doelmatige...
International audiencePhotonic delay systems have revolutionized the hardware implementation of Recu...
International audienceAn efficient photonic hardware integration of neural networks can benefit us f...
Reservoir computing is a brain-inspired approach for information processing, well suited to analog i...
Deep neural networks usually process information through multiple hidden layers. However, most hardw...
Machine Learning (ML) approaches like Deep Neural Networks (DNNs) have emerged as a powerful tool fo...
For many challenging problems where the mathematical description is not explicitly defined, artifici...
International audienceWe review a novel paradigm that has emerged in analogue neuromorphic optical c...
Driven by the remarkable breakthroughs during the past decade, photonics neural networks have experi...
Reservoir computing is a recent bio-inspired approach for processing time-dependent signals. It has ...
Reservoir Computing [1] is a new approach to study and use Neural Networks, which try to mimic a bra...
Reservoir computing (RC) is a technique in machine learning inspired by neural systems. RC has been ...
We study numerically a realistic model of an original autonomous implementation of a photonic neural...
In this thesis we study photonic computation within the framework of reservoir computing. Inspired b...
We present an autonomous all-photonic experimental implementation of an artificial neural network ba...
Als uitgangspunt fungeerde de vraag 'hoe op basis van toegekende rechtsaanspraken tot een doelmatige...
International audiencePhotonic delay systems have revolutionized the hardware implementation of Recu...
International audienceAn efficient photonic hardware integration of neural networks can benefit us f...