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Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere [article]

Pietro Verzelli and Cesare Alippi and Lorenzo Livi
2019 arXiv   pre-print
Among the various architectures of Recurrent Neural Networks, Echo State Networks (ESNs) emerged due to their simplified and inexpensive training procedure.  ...  These networks are known to be sensitive to the setting of hyper-parameters, which critically affect their behaviour.  ...  Acknowledgements (not compulsory) We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  ... 
arXiv:1903.11691v1 fatcat:ivpomppg4zdvjooprb6msdqlua

Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere

Pietro Verzelli, Cesare Alippi, Lorenzo Livi
2019 Scientific Reports  
Among the various architectures of Recurrent Neural Networks, Echo State Networks (ESNs) emerged due to their simplified and inexpensive training procedure.  ...  These networks are known to be sensitive to the setting of hyper-parameters, which critically affect their behavior.  ...  Acknowledgements We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  ... 
doi:10.1038/s41598-019-50158-4 pmid:31554855 pmcid:PMC6761167 fatcat:5xow22avvnbwjntdxv5vjhwkkm

Dam Inflow Prediction by using Artificial Neural Network Reservoir Computing

2019 International Journal of Engineering and Advanced Technology  
In this work, Context reverberation network, also known as reservoir computing approach, is applied for inflow forecasting. It comprises of a dynamic neural reservoir.  ...  So accurate inflow forecasting is necessary for effective water management. For inflow forecasting various methods are used by researchers.  ...  Neurons are sparsely connected to each other in a hyper spherical fashion and there can be self loops as well.  ... 
doi:10.35940/ijeat.b2990.129219 fatcat:ulcfvoehbvepxkpsjgrkqvkhee

Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization

Lorenzo Livi, Filippo Maria Bianchi, Cesare Alippi
2018 IEEE Transactions on Neural Networks and Learning Systems  
The paper takes advantage of a recently-developed non-parametric estimator of the Fisher information matrix and provides a method to determine the critical region of echo state networks, a particular class  ...  The considered control parameters, which indirectly affect the echo state network performance, are explored to identify those configurations lying on the edge of criticality and, as such, maximizing Fisher  ...  In this paper, we proposed a principled approach for configuring an echo state network on the edge of criticality.  ... 
doi:10.1109/tnnls.2016.2644268 pmid:28092580 fatcat:4tgpqjv5o5fwti4dpzhg53tqq4

A Full-Bandwidth Audio Codec With Low Complexity And Very Low Delay [article]

Jean-Marc Valin, Timothy B. Terriberry, Gregory Maxwell
2016 arXiv   pre-print
We propose an audio codec that addresses the low-delay requirements of some applications such as network music performance.  ...  The total complexity of the codec is small, at only 17 WMOPS for real-time operation at 48 kHz.  ...  The encoder and the decoder states are very small, requiring around 0.5 kByte for both states combined. The total amount of scratch space required is 7 kBytes.  ... 
arXiv:1602.05311v1 fatcat:zpfcl4bhubgtfero3262s4hzxy

Training submerged source detection for a 2D fluid flow sensor array with extreme learning machines

Ben J. Wolf, Sietse M. van Netten, Jianhong Zhou, Antanas Verikas, Petia Radeva, Dmitry P. Nikolaev
2019 Eleventh International Conference on Machine Vision (ICMV 2018)  
An MLP, echo state network (ESN), and extreme learning machine (ELM) were used to localise a source moving in a path.  ...  However, echo state networks and MLPs have been outperformed by ELMs for this path setting elsewhere [7] . Combining two sub tasks might have slightly impaired the estimation performance.  ... 
doi:10.1117/12.2522667 dblp:conf/icmv/WolfN18 fatcat:wgeuchvc2bbkhlp7osgs7thi3e

A Full-Bandwidth Audio Codec With Low Complexity And Very Low Delay

Gregory Maxwell, Timothy Terriberry, Jean-Marc Valin
2009 Zenodo  
The encoder and the decoder states are very small, requiring around 0.5 kByte for both states combined. The total amount of scratch space required is 7 kBytes.  ...  The mode flags are used for pre-echo avoidance and to signal the low-complexity mode described here.  ... 
doi:10.5281/zenodo.41340 fatcat:n7b6mozcfzeupgwaiopc47t7ki

A preliminary evaluation of the correlation between regional energy phosphates and resting state functional connectivity in depression

Chun S. Zuo, Pan Lin, Gordana Vitaliano, Kristina Wang, Rosemond Villafuerte, Scott E. Lukas
2015 NeuroImage: Clinical  
Resting state functional MRI (fMRI) has been established as an important tool for mapping cerebral regional activity and phosphorous chemical shift imaging ( 31 P CSI) has been applied to measure levels  ...  This is an initial attempt to identify the existence of a correlation between regional energy phosphates and connectivity at nodes of the posterior default mode network (DMN).  ...  The depressed DMN nodes in the PCC and posterior parietal cortex displayed a hyper-connective state that was associated with lower Pi/PCr ratios compared to those of the controls.  ... 
doi:10.1016/j.nicl.2015.08.020 pmid:26594618 pmcid:PMC4589842 fatcat:jo6ejlsj4rhgfoii2gtz3vyuqq

Magnetic Resonance Imaging of Structure and Coarsening in Three-Dimensional Foams

Burkhard A. Prause, James A. Glazier
2001 Proceedings of the 5th Experimental Chaos Conference  
We have developed an MRI technique to optimize the image quality for foams with very low liquid fraction.  ...  Manual extraction of vertex locations for several hundred bubbles provided exact bubble shapes and sizes, as well as the relation between a bubble's number of faces and volume.  ...  This grouping is required due to the non-spherical nature of the domains. Figure 4 .13 shows the nature of a distance map for polygonal bubbles.  ... 
doi:10.1142/9789812811516_0041 fatcat:vuwsnhqpyba5lltonmejmmape4

Contents

2012 Procedia Engineering  
Spherical Mobile Robot K.  ...  3-DOF Spherical Ultrasonic Motor B.  ... 
doi:10.1016/s1877-7058(12)01003-x fatcat:h3tienvctfgkpkwmhui6bgs4ee

Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs

Cristian Rotaru, Octav Brudaru
2012 2012 IEEE Congress on Evolutionary Computation  
This paper presents a cellular genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The evolution process is inspired by a basic genetic algorithm.  ...  Echo State Networks with a Feature Layer and a Nonlinear Readout 730, Oliver Obst and Martin Riedmiller, Taming the Reservoir Feedforward Training for Recurrent Neural Networks Tuesday, IJCNN, TuN 3-3  ...  Von Zuben, Performance Analysis of Nonlinear Echo State Network Readouts in Signal Processing Tasks 222, Long Ma, Chunheng Wang and Baihua Xiao, Sparse Representation based on Matrix Rank Minimization  ... 
doi:10.1109/cec.2012.6256590 dblp:conf/cec/RotaruB12 fatcat:4ly3nrktw5habc6lf5err7d5py

Applications and Techniques for Fast Machine Learning in Science [article]

Allison McCarn Deiana, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini (+74 others)
2021 arXiv   pre-print
The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for  ...  This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions.  ...  Among its variants are liquid state machines [637] , which is a spiking RC network, and echo state networks [638] , an RC based on a very sparse recurrent network.  ... 
arXiv:2110.13041v1 fatcat:cvbo2hmfgfcuxi7abezypw2qrm

Membrane tension and cytoskeleton organization in cell motility

Pierre Sens, Julie Plastino
2015 Journal of Physics: Condensed Matter  
For a long time, the membrane was viewed as a relatively passive scaffold for signaling.  ...  Various disease states are associated with deregulation of how cells move and change shape, including notably tumor initiation and cancer cell metastasis.  ...  Acknowledgments We acknowledge Dr Agnieszka Kawska at IlluScientia.com for graphical design of the figures. We thank Cécile Sykes (Institut Curie) for critical reading of the manuscript.  ... 
doi:10.1088/0953-8984/27/27/273103 pmid:26061624 fatcat:f3fpp44uffbh5dhcdgfm5jryay

Towards Comprehensive Foundations of Computational Intelligence [chapter]

Włodzisław Duch
2007 Studies in Computational Intelligence  
Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for  ...  The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power  ...  The Echo State Networks, or more general Reservoir Computing, use untrained recurrent neural networks as "reservoirs of activity" to implement this projection.  ... 
doi:10.1007/978-3-540-71984-7_11 fatcat:vlfuzhxmgbbqlptp4wnpdty4ae

2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31

2020 IEEE Transactions on Neural Networks and Learning Systems  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TNNLS May 2020 1489-1503 Evolving Local Plasticity Rules for Synergistic Learning in Echo State Networks.  ...  Chen, X., +, TNNLS July 2020 2441-2454 Evolving Local Plasticity Rules for Synergistic Learning in Echo State Networks.  ... 
doi:10.1109/tnnls.2020.3045307 fatcat:34qoykdtarewhdscxqj5jvovqy
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