International audienceThis paper deals with two ideas appeared during the last developing phase in Artificial Intelligence: Reservoir Computing and Random Neural Networks. Both have been very successful in many applications. We propose a new model belonging to the first class, taking the structure of the second for its dynamics. The new model is called Echo State Queuing Network. The paper positions the model in the global Machine Learning area, and provides examples of its use and performances. We show on largely used benchmarks that it is a very accurate tool, and we illustrate how it compares with standard Reservoi
Reservoir computing (RC) studies the properties of large recurrent networks of artificial neurons, w...
Recurrent neural networks are successfully used for tasks like time series processing and system ide...
Echo State Networks (ESNs) were introduced to simplify the design and training of Recurrent Neural N...
Abstract—In the last decade, a new computational paradigm was introduced in the field of Machine Lea...
Au cours de ces dernières années, un nouveau paradigme a été introduit dans le domaine de l'apprenti...
In this paper, we present a novel architecture and learning algorithm for a multilayered echo state ...
Echo State Networks are a model used for supervised learning since the 2000s. This paper presents a ...
The increasing role of Artificial Intelligence (AI) and Machine Learning (ML) in our lives brought a...
Context of the tutorial: the IEEE CIS Summer School on Computational Intelligence and Applications (...
Echo State Networks and Liquid State Machines introduced a new paradigm in artificial recurrent neur...
Differentiable neural computers extend artificial neural networks with an explicit memory without in...
In this paper, we formally deduce a new computational model, with a recurrent structure, by means of...
In a network of agents, a widespread problem is the need to estimate a common underlying function st...
Recurrent Neural Networks (RNNs) are amongst the most powerful Machine Learning models to deal with ...
Paaßen B, Schulz A. Reservoir memory machines. In: Verleysen M, ed. Proceedings of the 28th European...
Reservoir computing (RC) studies the properties of large recurrent networks of artificial neurons, w...
Recurrent neural networks are successfully used for tasks like time series processing and system ide...
Echo State Networks (ESNs) were introduced to simplify the design and training of Recurrent Neural N...
Abstract—In the last decade, a new computational paradigm was introduced in the field of Machine Lea...
Au cours de ces dernières années, un nouveau paradigme a été introduit dans le domaine de l'apprenti...
In this paper, we present a novel architecture and learning algorithm for a multilayered echo state ...
Echo State Networks are a model used for supervised learning since the 2000s. This paper presents a ...
The increasing role of Artificial Intelligence (AI) and Machine Learning (ML) in our lives brought a...
Context of the tutorial: the IEEE CIS Summer School on Computational Intelligence and Applications (...
Echo State Networks and Liquid State Machines introduced a new paradigm in artificial recurrent neur...
Differentiable neural computers extend artificial neural networks with an explicit memory without in...
In this paper, we formally deduce a new computational model, with a recurrent structure, by means of...
In a network of agents, a widespread problem is the need to estimate a common underlying function st...
Recurrent Neural Networks (RNNs) are amongst the most powerful Machine Learning models to deal with ...
Paaßen B, Schulz A. Reservoir memory machines. In: Verleysen M, ed. Proceedings of the 28th European...
Reservoir computing (RC) studies the properties of large recurrent networks of artificial neurons, w...
Recurrent neural networks are successfully used for tasks like time series processing and system ide...
Echo State Networks (ESNs) were introduced to simplify the design and training of Recurrent Neural N...