Architectural richness in deep reservoir computing

Abstract Reservoir computing (RC) is a popular class of recurrent neural networks (RNNs) with untrained dynamics. Recently, advancements on deep RC architectures have shown a great impact in time-series applications, showing a convenient trade-off between predictive performance and required training...
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Gespeichert in:

Autor*in:	Gallicchio, Claudio [verfasserIn] Micheli, Alessio

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Reservoir computing Deep reservoir computing Deep recurrent neural networks Richness of neural networks dynamics

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - London : Springer, 1993, 35(2022), 34 vom: 15. Jan., Seite 24525-24542
Übergeordnetes Werk:	volume:35 ; year:2022 ; number:34 ; day:15 ; month:01 ; pages:24525-24542

Links:	Volltext

DOI / URN:	10.1007/s00521-021-06760-7

Katalog-ID:	SPR053616790

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allfields	10.1007/s00521-021-06760-7 doi (DE-627)SPR053616790 (SPR)s00521-021-06760-7-e DE-627 ger DE-627 rakwb eng Gallicchio, Claudio verfasserin (orcid)0000-0002-6692-2564 aut Architectural richness in deep reservoir computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Reservoir computing (RC) is a popular class of recurrent neural networks (RNNs) with untrained dynamics. Recently, advancements on deep RC architectures have shown a great impact in time-series applications, showing a convenient trade-off between predictive performance and required training complexity. In this paper, we go more in depth into the analysis of untrained RNNs by studying the quality of recurrent dynamics developed by the layers of deep RC neural networks. We do so by assessing the richness of the neural representations in the different levels of the architecture, using measures originating from the fields of dynamical systems, numerical analysis and information theory. Our experiments, on both synthetic and real-world datasets, show that depth—as an architectural factor of RNNs design—has a natural effect on the quality of RNN dynamics (even without learning of the internal connections). The interplay between depth and the values of RC scaling hyper-parameters, especially the scaling of inter-layer connections, is crucial to design rich untrained recurrent neural systems. Reservoir computing (dpeaa)DE-He213 Deep reservoir computing (dpeaa)DE-He213 Deep recurrent neural networks (dpeaa)DE-He213 Richness of neural networks dynamics (dpeaa)DE-He213 Micheli, Alessio aut Enthalten in Neural computing & applications London : Springer, 1993 35(2022), 34 vom: 15. Jan., Seite 24525-24542 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2022 number:34 day:15 month:01 pages:24525-24542 https://dx.doi.org/10.1007/s00521-021-06760-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 34 15 01 24525-24542
spelling	10.1007/s00521-021-06760-7 doi (DE-627)SPR053616790 (SPR)s00521-021-06760-7-e DE-627 ger DE-627 rakwb eng Gallicchio, Claudio verfasserin (orcid)0000-0002-6692-2564 aut Architectural richness in deep reservoir computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Reservoir computing (RC) is a popular class of recurrent neural networks (RNNs) with untrained dynamics. Recently, advancements on deep RC architectures have shown a great impact in time-series applications, showing a convenient trade-off between predictive performance and required training complexity. In this paper, we go more in depth into the analysis of untrained RNNs by studying the quality of recurrent dynamics developed by the layers of deep RC neural networks. We do so by assessing the richness of the neural representations in the different levels of the architecture, using measures originating from the fields of dynamical systems, numerical analysis and information theory. Our experiments, on both synthetic and real-world datasets, show that depth—as an architectural factor of RNNs design—has a natural effect on the quality of RNN dynamics (even without learning of the internal connections). The interplay between depth and the values of RC scaling hyper-parameters, especially the scaling of inter-layer connections, is crucial to design rich untrained recurrent neural systems. Reservoir computing (dpeaa)DE-He213 Deep reservoir computing (dpeaa)DE-He213 Deep recurrent neural networks (dpeaa)DE-He213 Richness of neural networks dynamics (dpeaa)DE-He213 Micheli, Alessio aut Enthalten in Neural computing & applications London : Springer, 1993 35(2022), 34 vom: 15. Jan., Seite 24525-24542 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2022 number:34 day:15 month:01 pages:24525-24542 https://dx.doi.org/10.1007/s00521-021-06760-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 34 15 01 24525-24542
allfields_unstemmed	10.1007/s00521-021-06760-7 doi (DE-627)SPR053616790 (SPR)s00521-021-06760-7-e DE-627 ger DE-627 rakwb eng Gallicchio, Claudio verfasserin (orcid)0000-0002-6692-2564 aut Architectural richness in deep reservoir computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Reservoir computing (RC) is a popular class of recurrent neural networks (RNNs) with untrained dynamics. Recently, advancements on deep RC architectures have shown a great impact in time-series applications, showing a convenient trade-off between predictive performance and required training complexity. In this paper, we go more in depth into the analysis of untrained RNNs by studying the quality of recurrent dynamics developed by the layers of deep RC neural networks. We do so by assessing the richness of the neural representations in the different levels of the architecture, using measures originating from the fields of dynamical systems, numerical analysis and information theory. Our experiments, on both synthetic and real-world datasets, show that depth—as an architectural factor of RNNs design—has a natural effect on the quality of RNN dynamics (even without learning of the internal connections). The interplay between depth and the values of RC scaling hyper-parameters, especially the scaling of inter-layer connections, is crucial to design rich untrained recurrent neural systems. Reservoir computing (dpeaa)DE-He213 Deep reservoir computing (dpeaa)DE-He213 Deep recurrent neural networks (dpeaa)DE-He213 Richness of neural networks dynamics (dpeaa)DE-He213 Micheli, Alessio aut Enthalten in Neural computing & applications London : Springer, 1993 35(2022), 34 vom: 15. Jan., Seite 24525-24542 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2022 number:34 day:15 month:01 pages:24525-24542 https://dx.doi.org/10.1007/s00521-021-06760-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 34 15 01 24525-24542
allfieldsGer	10.1007/s00521-021-06760-7 doi (DE-627)SPR053616790 (SPR)s00521-021-06760-7-e DE-627 ger DE-627 rakwb eng Gallicchio, Claudio verfasserin (orcid)0000-0002-6692-2564 aut Architectural richness in deep reservoir computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Reservoir computing (RC) is a popular class of recurrent neural networks (RNNs) with untrained dynamics. Recently, advancements on deep RC architectures have shown a great impact in time-series applications, showing a convenient trade-off between predictive performance and required training complexity. In this paper, we go more in depth into the analysis of untrained RNNs by studying the quality of recurrent dynamics developed by the layers of deep RC neural networks. We do so by assessing the richness of the neural representations in the different levels of the architecture, using measures originating from the fields of dynamical systems, numerical analysis and information theory. Our experiments, on both synthetic and real-world datasets, show that depth—as an architectural factor of RNNs design—has a natural effect on the quality of RNN dynamics (even without learning of the internal connections). The interplay between depth and the values of RC scaling hyper-parameters, especially the scaling of inter-layer connections, is crucial to design rich untrained recurrent neural systems. Reservoir computing (dpeaa)DE-He213 Deep reservoir computing (dpeaa)DE-He213 Deep recurrent neural networks (dpeaa)DE-He213 Richness of neural networks dynamics (dpeaa)DE-He213 Micheli, Alessio aut Enthalten in Neural computing & applications London : Springer, 1993 35(2022), 34 vom: 15. Jan., Seite 24525-24542 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2022 number:34 day:15 month:01 pages:24525-24542 https://dx.doi.org/10.1007/s00521-021-06760-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 34 15 01 24525-24542
allfieldsSound	10.1007/s00521-021-06760-7 doi (DE-627)SPR053616790 (SPR)s00521-021-06760-7-e DE-627 ger DE-627 rakwb eng Gallicchio, Claudio verfasserin (orcid)0000-0002-6692-2564 aut Architectural richness in deep reservoir computing 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract Reservoir computing (RC) is a popular class of recurrent neural networks (RNNs) with untrained dynamics. Recently, advancements on deep RC architectures have shown a great impact in time-series applications, showing a convenient trade-off between predictive performance and required training complexity. In this paper, we go more in depth into the analysis of untrained RNNs by studying the quality of recurrent dynamics developed by the layers of deep RC neural networks. We do so by assessing the richness of the neural representations in the different levels of the architecture, using measures originating from the fields of dynamical systems, numerical analysis and information theory. Our experiments, on both synthetic and real-world datasets, show that depth—as an architectural factor of RNNs design—has a natural effect on the quality of RNN dynamics (even without learning of the internal connections). The interplay between depth and the values of RC scaling hyper-parameters, especially the scaling of inter-layer connections, is crucial to design rich untrained recurrent neural systems. Reservoir computing (dpeaa)DE-He213 Deep reservoir computing (dpeaa)DE-He213 Deep recurrent neural networks (dpeaa)DE-He213 Richness of neural networks dynamics (dpeaa)DE-He213 Micheli, Alessio aut Enthalten in Neural computing & applications London : Springer, 1993 35(2022), 34 vom: 15. Jan., Seite 24525-24542 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:35 year:2022 number:34 day:15 month:01 pages:24525-24542 https://dx.doi.org/10.1007/s00521-021-06760-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 35 2022 34 15 01 24525-24542
language	English
source	Enthalten in Neural computing & applications 35(2022), 34 vom: 15. Jan., Seite 24525-24542 volume:35 year:2022 number:34 day:15 month:01 pages:24525-24542
sourceStr	Enthalten in Neural computing & applications 35(2022), 34 vom: 15. Jan., Seite 24525-24542 volume:35 year:2022 number:34 day:15 month:01 pages:24525-24542
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Reservoir computing Deep reservoir computing Deep recurrent neural networks Richness of neural networks dynamics
isfreeaccess_bool	false
container_title	Neural computing & applications
authorswithroles_txt_mv	Gallicchio, Claudio @@aut@@ Micheli, Alessio @@aut@@
publishDateDaySort_date	2022-01-15T00:00:00Z
hierarchy_top_id	271595574
id	SPR053616790
language_de	englisch
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author	Gallicchio, Claudio
spellingShingle	Gallicchio, Claudio misc Reservoir computing misc Deep reservoir computing misc Deep recurrent neural networks misc Richness of neural networks dynamics Architectural richness in deep reservoir computing
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topic_title	Architectural richness in deep reservoir computing Reservoir computing (dpeaa)DE-He213 Deep reservoir computing (dpeaa)DE-He213 Deep recurrent neural networks (dpeaa)DE-He213 Richness of neural networks dynamics (dpeaa)DE-He213
topic	misc Reservoir computing misc Deep reservoir computing misc Deep recurrent neural networks misc Richness of neural networks dynamics
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title_sort	architectural richness in deep reservoir computing
title_auth	Architectural richness in deep reservoir computing
abstract	Abstract Reservoir computing (RC) is a popular class of recurrent neural networks (RNNs) with untrained dynamics. Recently, advancements on deep RC architectures have shown a great impact in time-series applications, showing a convenient trade-off between predictive performance and required training complexity. In this paper, we go more in depth into the analysis of untrained RNNs by studying the quality of recurrent dynamics developed by the layers of deep RC neural networks. We do so by assessing the richness of the neural representations in the different levels of the architecture, using measures originating from the fields of dynamical systems, numerical analysis and information theory. Our experiments, on both synthetic and real-world datasets, show that depth—as an architectural factor of RNNs design—has a natural effect on the quality of RNN dynamics (even without learning of the internal connections). The interplay between depth and the values of RC scaling hyper-parameters, especially the scaling of inter-layer connections, is crucial to design rich untrained recurrent neural systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
abstractGer	Abstract Reservoir computing (RC) is a popular class of recurrent neural networks (RNNs) with untrained dynamics. Recently, advancements on deep RC architectures have shown a great impact in time-series applications, showing a convenient trade-off between predictive performance and required training complexity. In this paper, we go more in depth into the analysis of untrained RNNs by studying the quality of recurrent dynamics developed by the layers of deep RC neural networks. We do so by assessing the richness of the neural representations in the different levels of the architecture, using measures originating from the fields of dynamical systems, numerical analysis and information theory. Our experiments, on both synthetic and real-world datasets, show that depth—as an architectural factor of RNNs design—has a natural effect on the quality of RNN dynamics (even without learning of the internal connections). The interplay between depth and the values of RC scaling hyper-parameters, especially the scaling of inter-layer connections, is crucial to design rich untrained recurrent neural systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
abstract_unstemmed	Abstract Reservoir computing (RC) is a popular class of recurrent neural networks (RNNs) with untrained dynamics. Recently, advancements on deep RC architectures have shown a great impact in time-series applications, showing a convenient trade-off between predictive performance and required training complexity. In this paper, we go more in depth into the analysis of untrained RNNs by studying the quality of recurrent dynamics developed by the layers of deep RC neural networks. We do so by assessing the richness of the neural representations in the different levels of the architecture, using measures originating from the fields of dynamical systems, numerical analysis and information theory. Our experiments, on both synthetic and real-world datasets, show that depth—as an architectural factor of RNNs design—has a natural effect on the quality of RNN dynamics (even without learning of the internal connections). The interplay between depth and the values of RC scaling hyper-parameters, especially the scaling of inter-layer connections, is crucial to design rich untrained recurrent neural systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
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container_issue	34
title_short	Architectural richness in deep reservoir computing
url	https://dx.doi.org/10.1007/s00521-021-06760-7
remote_bool	true
author2	Micheli, Alessio
author2Str	Micheli, Alessio
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doi_str	10.1007/s00521-021-06760-7
up_date	2024-07-03T20:48:53.999Z
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