Herbert Jaeger

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Mantas LukoševičiusKaunas University of TechnologyVerified email at ktu.lt
Harald HaasThe University of CambridgeVerified email at cam.ac.uk
Benjamin SchrauwenProfessor of Machine Learning, Ghent UniversityVerified email at ugent.be
Manjunath GUniversity of PretroriaVerified email at up.ac.za
Xu Owen HeGoogle DeepMindVerified email at google.com
Paul PloegerProfessor for Autonomous SystemsVerified email at h-brs.de
Stefan KiebelProfessor of Neuroimaging, Technische Universität Dresden, GermanyVerified email at tu-dresden.de
Michael ThonJacobs University BremenVerified email at jacobs-university.de
jose principeprofessor of ECE, U. of FloridaVerified email at cnel.ufl.edu
Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
Joachim HertzbergUniversity of Osnabrück and DFKI, OsnabrückVerified email at uos.de
Fatemeh HadaeghiUniversitätsklinikum Hamburg-Eppendorf (UKE)Verified email at uke.de
Andreas KollingAmazon RoboticsVerified email at amazon.com
Razvan PascanuGoogle DeepMindVerified email at google.com
Wilfred G. van der WielMESA+ Inst. for Nanotechnology, BRAINS, University of TwenteVerified email at utwente.nl
Beatriz NohedaZernike Institute for Advanced Materials & CogniGron center, University of GroningenVerified email at rug.nl
Luc SteelsICREA - Institut de Biologia Evolutiva (UPF-CSIC)Verified email at upf.edu
Ted BriscoeProfessor of Computational Linguistics, MBZUAIVerified email at mbzuai.ac.ae
Gerhard JägerProfessor für Allgemeine Sprachwissenschaft, TübingenVerified email at uni-tuebingen.de
Jochen TrieschFrankfurt Institute for Advanced StudiesVerified email at fias.uni-frankfurt.de

Herbert Jaeger

Rijksuniversiteit Groningen, CogniGron Center

Verified email at rug.nl - Homepage

Recurrent neural networks machine learning stochastic dynamical systems theory of non-symbolic


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The “echo state” approach to analysing and training recurrent neural networks-with an erratum note H Jaeger Bonn, Germany: German National Research Center for Information Technology …, 2001	4393	2001
Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication H Jaeger, H Haas science 304 (5667), 78-80, 2004	4004	2004
Reservoir computing approaches to recurrent neural network training M Lukoševičius, H Jaeger Computer science review 3 (3), 127-149, 2009	3126	2009
Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the echo state network approach H Jaeger GMD-Forschungszentrum Informationstechnik 5 (1), 2002	1466	2002
Optimization and applications of echo state networks with leaky-integrator neurons H Jaeger, M Lukoševičius, D Popovici, U Siewert Neural networks 20 (3), 335-352, 2007	1035	2007
Short term memory in echo state networks. gmd-report 152 H Jaeger GMD-German National Research Institute for Computer Science (2002), 2002	959*	2002
Adaptive nonlinear system identification with echo state networks H Jaeger Advances in neural information processing systems 15, 2002	850	2002
Re-visiting the echo state property IB Yildiz, H Jaeger, SJ Kiebel Neural networks 35, 1-9, 2012	516	2012
Reservoir computing trends M Lukoševičius, H Jaeger, B Schrauwen KI-Künstliche Intelligenz 26, 365-371, 2012	459	2012
Echo state network H Jaeger scholarpedia 2 (9), 2330, 2007	449	2007
Observable operator models for discrete stochastic time series H Jaeger Neural computation 12 (6), 1371-1398, 2000	254	2000
Echo state property linked to an input: Exploring a fundamental characteristic of recurrent neural networks G Manjunath, H Jaeger Neural computation 25 (3), 671-696, 2013	230	2013
Special issue on echo state networks and liquid state machines. H Jaeger, W Maass, J Principe Elsevier Science, 2007	215	2007
Reservoir riddles: suggestions for echo state network research H Jaeger Proceedings. 2005 IEEE International Joint Conference on Neural Networks …, 2005	151	2005
Dual dynamics: Designing behavior systems for autonomous robots H Jaeger, T Christaller Artificial Life and Robotics 2, 108-112, 1998	132	1998
Controlling recurrent neural networks by conceptors H Jaeger arXiv preprint arXiv:1403.3369, 2014	130	2014
Discovering multiscale dynamical features with hierarchical echo state networks H Jaeger Jacobs University Bremen, 2007	129	2007
Overcoming catastrophic interference using conceptor-aided backpropagation X He, H Jaeger International Conference on Learning Representations, 2018	117	2018
A neurodynamical model for working memory R Pascanu, H Jaeger Neural networks 24 (2), 199-207, 2011	109	2011
Long short-term memory in echo state networks: Details of a simulation study H Jaeger Jacobs University Bremen, 2012	91	2012

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