Herbert Jaeger
TitleCited byYear
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
20162001
Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
H Jaeger, H Haas
science 304 (5667), 78-80, 2004
19532004
Reservoir computing approaches to recurrent neural network training
M Lukoševičius, H Jaeger
Computer Science Review 3 (3), 127-149, 2009
12542009
Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the" echo state network" approach
H Jaeger
GMD-Forschungszentrum Informationstechnik 5, 01, 2002
8382002
Short term memory in echo state networks
H Jaeger
GMD-Forschungszentrum Informationstechnik, 2001
5212001
Adaptive nonlinear system identification with echo state networks
H Jaeger
Advances in neural information processing systems, 609-616, 2003
5152003
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
5122007
Observable operator models for discrete stochastic time series
H Jaeger
Neural Computation 12 (6), 1371-1398, 2000
1892000
Reservoir computing trends
M Lukoševičius, H Jaeger, B Schrauwen
KI-Künstliche Intelligenz 26 (4), 365-371, 2012
1862012
Re-visiting the echo state property
IB Yildiz, H Jaeger, SJ Kiebel
Neural networks 35, 1-9, 2012
1672012
Echo state network
H Jaeger
Scholarpedia 2 (9), 2330, 2007
1652007
Special issue on echo state networks and liquid state machines.
H Jaeger, W Maass, J Principe
Elsevier Science, 2007
1412007
Dual dynamics: Designing behavior systems for autonomous robots
H Jaeger, T Christaller
Artificial Life and Robotics 2 (3), 108-112, 1998
1191998
Reservoir riddles: Suggestions for echo state network research
H Jaeger
Proceedings. 2005 IEEE International Joint Conference on Neural Networks …, 2005
1122005
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
922013
Discovering multiscale dynamical features with hierarchical echo state networks
H Jaeger
Jacobs University Bremen, 2007
692007
Controlling recurrent neural networks by conceptors
H Jaeger
arXiv preprint arXiv:1403.3369, 2014
622014
A neurodynamical model for working memory
R Pascanu, H Jaeger
Neural networks 24 (2), 199-207, 2011
572011
Tutorial on training recurrent neural networks, covering bppt, rtrl, ekf and the"‘echo state network
H Jaeger
Gesellschaft für Mathematik und Datenverarbeitung Report 159, 2002
502002
Discrete-time, discrete-valued observable operator models: a tutorial
H Jaeger
GMD-Forschungszentrum Informationstechnik, 1998
451998
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Articles 1–20