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Towards Deep Physical Reservoir Computing Through Automatic Task Decomposition And Mapping
[article]
2019
arXiv
pre-print
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Therefore, a switch from single-reservoir computing to multi-reservoir and even deep physical reservoir computing is desirable. ...
Photonic reservoir computing is a promising candidate for low-energy computing at high bandwidths. ...
step (using different, randomly initialized ESNs) as a whole using backpropagation to automatically find a good task decomposition. ...
arXiv:1910.13332v1
fatcat:wgsv36xtozghbc6fatlrzlwile
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs
[article]
2018
arXiv
pre-print
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In this work, we propose a new approach towards the efficient optimization and implementation of reservoir computing hardware reducing the required domain expert knowledge and optimization effort. ...
We confirm the validity of those principles building reservoir computing hardware based on a field-programmable gate array. ...
ANR-11-LABX-0001-01) and by the BiPhoProc ANR project (ANR-14-OHRI-0002-02). ...
arXiv:1805.03033v1
fatcat:hunx4rn3dveu7meluo5wvuolli
A Review of Designs and Applications of Echo State Networks
[article]
2020
arXiv
pre-print
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Since ESN was put forward in 2002, abundant existing works have promoted the progress of ESN, and the recently introduced Deep ESN model opened the way to uniting the merits of deep learning and ESNs. ...
Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence tasks and have achieved state-of-the-art in wide range of applications, such as industrial, medical, economic and ...
Deep echo state networks With the development of deep learning [99] , Stacking architecture [100] has been introduced into reservoir computing. ...
arXiv:2012.02974v1
fatcat:ngp7zdv4y5cgnbnrq6ibbdmp6y
A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources
[article]
2020
arXiv
pre-print
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Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry ...
The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide ...
Toward this end, many researchers have applied cutting-edge deep learning architectures to the runoff prediction and flood forecasting tasks. ...
arXiv:2007.12269v1
fatcat:7vc2r76fozhtpcveli6h4uldie
Role of non-linear data processing on speech recognition task in the framework of reservoir computing
[article]
2019
arXiv
pre-print
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One of the preferred tasks for bench-marking such devices is automatic speech recognition. ...
The reservoir computing neural network architecture is widely used to test hardware systems for neuromorphic computing. ...
Rather than a set of N θ physical neurons, our reservoir consists of a single physical neuron evaluated at N θ periodic times. ...
arXiv:1906.02812v3
fatcat:dvvh4pr26zfq5dqu7fd7ozgzpe
A new method for extracting knowledge from patents to inspire designers during the problem-solving phase
2017
Journal of engineering design
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Many different approaches are more oriented towards the automation of complex tasks, such as analysis and knowledge extraction through computer science and artificial intelligence techniques. ...
All of the developed modules have an automatic part to facilitate the task, but the analysis of patents, the identification of pertinent keywords, and the functional decomposition must be done manually ...
doi:10.1080/09544828.2017.1316361
fatcat:wd7iwjlgmzaathrxxnlhe3qvde
Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges
2020
Proceedings of the IEEE
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2021 pre-print arXiv:2108.03383v1 fatcat:7cbkssngwjdy5dmsiwyo6hh5vq
., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. ...
The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. ...
The mechanism of self-organizing schedules for multiple production tasks can be divided into three steps: task analysis, task decomposition, and task execution. ...
doi:10.1109/jproc.2020.3034808
fatcat:bpljlzguqjhypedblczhmch2uq
2022 Roadmap on Neuromorphic Computing and Engineering
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2105.05956v2 fatcat:n5zeug6lnfgn3jvmnh2w6sckzyOther Versions
Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically ...
This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. ...
This new class of extremely low-power and lowlatency artificial intelligence systems could, In a world where power-hungry deep learning techniques are becoming a commodity, and at the same time, environmental ...
arXiv:2105.05956v3
fatcat:pqir5infojfpvdzdwgmwdhsdi4
Data-driven geophysics: from dictionary learning to deep learning
[article]
2020
arXiv
pre-print
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Understanding the principles of geophysical phenomena is an essential and challenging task. ...
We present a coding tutorial and a summary of tips for beginners and interested geophysical readers to rapidly explore deep learning. ...
Acknowledgments The work was supported in part by the National Key Research and Development Program
Data Availability Statement Data were not used, nor created for this research. ...
arXiv:2007.06183v2
fatcat:ow45ejo7izbkpmssedwd74rbym
Leveraging AI in Photonics and Beyond
2022
Photonics
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This article reviews leveraging AI in photonics modeling, simulation, and inverse design; leveraging photonics computing for implementing AI algorithms; and leveraging AI beyond photonics topics, such ...
Photonics benefit a great deal from AI, and AI, in turn, benefits from photonics by carrying out AI algorithms, such as complicated deep neural networks using photonics components that use photons rather ...
Reservoir Computing Reservoir computing is a framework that is closely related to recurrent neural networks. ...
doi:10.3390/photonics9020075
fatcat:pkomp66omrg6hc7ydm7byetjua
Model-Based Design and Formal Verification Processes for Automated Waterway System Operations
2016
Systems
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In a step toward overcoming these challenges, this paper argues that programs for waterway and canal modernization and sustainability can benefit significantly from system thinking, supported by systems ...
Failures, delays, incidents and accidents in aging waterway systems are doing little to attract the technical and economic assistance required for modernization and sustainability. ...
Inputs and outputs: Ships are physical entities that enter and exit the waterway through the above-mentioned boundaries. ...
doi:10.3390/systems4020023
fatcat:krzvtb25ezhntgtteq7nez6ug4
Sample-level sound synthesis with recurrent neural networks and conceptors
2019
PeerJ Computer Science
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Conceptors are a recent development in the field of reservoir computing; they can be used to influence the dynamics of recurrent neural networks (RNNs), enabling generation of arbitrary patterns based ...
Limitations of conceptor models are revealed with regards to reproduction quality, and pragmatic limitations are also shown, where rises in computation and memory requirements preclude the use of these ...
ACKNOWLEDGEMENTS Thank you to Sussex Humanities Lab for generous access of their computing facilities.
ADDITIONAL INFORMATION AND DECLARATIONS Funding The author received no funding for this work. ...
doi:10.7717/peerj-cs.205
pmid:33816858
pmcid:PMC7924416
fatcat:fo4rmbabdjfafapjkeajnpmdpu
Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
[article]
2021
arXiv
pre-print
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high-performance computer. ...
Consequently, gradients of our DA objective function with respect to the decision variables are obtained rapidly via automatic differentiation. ...
Morever, in contrast with [41] where a reservoir computer was used as the surrogate, our ML emulator is given by deep recurrent neural network (i.e., a long shortterm memory neural network with several ...
arXiv:2112.07856v1
fatcat:w5zyadkiwvgunbjvwcrpwkx5wq
Table of Contents
2020
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
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Olbricht .......... 513 Towards a Data-Driven Fuzzy-Geospatial Pandemic Modelling Amir Pourabdollah and Ahmad Lotfi .......... 521 Who is physically active? ...
1710 Towards Potential of N-back Task as Protocol and EEGNet for the EEG-based Biometric Nima Salimi, Michael Barlow and Erandi Lakshika .......... 1718 Efficient Method for High-Resolution Fingerprint ...
doi:10.1109/ssci47803.2020.9308155
fatcat:hyargfnk4vevpnooatlovxm4li
Evidence from three-dimensional seismic tomography for a substantial accumulation of gas hydrate in a fluid-escape chimney in the Nyegga pockmark field, offshore Norway
2010
Journal of Geophysical Research
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Single channel seismic, ocean bottom seismic recorders (OBS), 3D high resolution P-Cable seismic and bathymetry data of the study area were acquired during cruises in July 2006 and 2008 on board R/V Jan ...
Ocean bottom seismic recorders (OBSs) were kindly provided by UK Ocean Bottom Instrumentation Consortium and by IFREMER. ...
Many of them shared with family, friends and colleagues, and as such they are reviewed in the following acknowledgements. ...
doi:10.1029/2009jb007078
fatcat:j3truqv6tbhk5bilabicttnfce
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