scholarly journals Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

2021 ◽  
pp. 2100027
Author(s):  
Pere Mujal ◽  
Rodrigo Martínez‐Peña ◽  
Johannes Nokkala ◽  
Jorge García‐Beni ◽  
Gian Luca Giorgi ◽  
...  
Keyword(s):  
Reservoir Computing ◽  
Extreme Learning Machines ◽  
Learning Machines

scholarly journals A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
S. Ortín ◽  
M. C. Soriano ◽  
L. Pesquera ◽  
D. Brunner ◽  
D. San-Martín ◽  
...  
Keyword(s):  
Reservoir Computing ◽  
Extreme Learning Machines ◽  
Unified Framework ◽  
Single Time ◽  
Learning Machines

Unorganized Machines

2014 ◽  
pp. 221-236 ◽  
Author(s):  
Levy Boccato ◽  
Everton S. Soares ◽  
Marcos M. L. P. Fernandes ◽  
Diogo C. Soriano ◽  
Romis Attux
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reservoir Computing ◽  
State Machines ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Alan Turing ◽  
Echo State Networks ◽  
The Relationship ◽  
And Training

This work presents a discussion about the relationship between the contributions of Alan Turing – the centenary of whose birth is celebrated in 2012 – to the field of artificial neural networks and modern unorganized machines: reservoir computing (RC) approaches and extreme learning machines (ELMs). Firstly, the authors review Turing’s connectionist proposals and also expose the fundamentals of the main RC paradigms – echo state networks and liquid state machines, - as well as of the design and training of ELMs. Throughout this exposition, the main points of contact between Turing’s ideas and these modern perspectives are outlined, being, then, duly summarized in the second and final part of the work. This paper is useful in offering a distinct appreciation of Turing’s pioneering contributions to the field of neural networks and also in indicating some perspectives for the future development of the field that may arise from the synergy between these views.

Reservoir computing and extreme learning machines for non-linear time-series data analysis

2013 ◽  
Vol 38 ◽  
pp. 76-89 ◽  
Author(s):  
J.B. Butcher ◽  
D. Verstraeten ◽  
B. Schrauwen ◽  
C.R. Day ◽  
P.W. Haycock
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Linear Time ◽  
Series Data ◽  
Reservoir Computing ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Non Linear ◽  
Time Series Data Analysis

Unorganized Machines

2011 ◽  
Vol 2 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Levy Boccato ◽  
Everton S. Soares ◽  
Marcos M. L. P. Fernandes ◽  
Diogo C. Soriano ◽  
Romis Attux
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reservoir Computing ◽  
State Machines ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Alan Turing ◽  
Echo State Networks ◽  
The Relationship ◽  
And Training

This work presents a discussion about the relationship between the contributions of Alan Turing – the centenary of whose birth is celebrated in 2012 – to the field of artificial neural networks and modern unorganized machines: reservoir computing (RC) approaches and extreme learning machines (ELMs). Firstly, the authors review Turing’s connectionist proposals and also expose the fundamentals of the main RC paradigms – echo state networks and liquid state machines, - as well as of the design and training of ELMs. Throughout this exposition, the main points of contact between Turing’s ideas and these modern perspectives are outlined, being, then, duly summarized in the second and final part of the work. This paper is useful in offering a distinct appreciation of Turing’s pioneering contributions to the field of neural networks and also in indicating some perspectives for the future development of the field that may arise from the synergy between these views.

Extreme learning machines’ ensemble selection with GRASP

2015 ◽  
Vol 43 (2) ◽  
pp. 439-459 ◽  
Author(s):  
Ting Zhang ◽  
Qun Dai ◽  
Zhongchen Ma
Keyword(s):  
Extreme Learning Machines ◽  
Learning Machines ◽  
Ensemble Selection
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