Photonic delay systems as machine learning implementations
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[1] Benjamin Schrauwen,et al. Training and Analysing Deep Recurrent Neural Networks , 2013, NIPS.
[2] Benjamin Schrauwen,et al. Locomotion Without a Brain: Physical Reservoir Computing in Tensegrity Structures , 2013, Artificial Life.
[3] Jürgen Schmidhuber,et al. Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[4] Ilya Sutskever,et al. Learning Recurrent Neural Networks with Hessian-Free Optimization , 2011, ICML.
[5] Harald Haas,et al. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.
[6] Jan Danckaert,et al. Constructing optimized binary masks for reservoir computing with delay systems , 2014, Scientific Reports.
[7] Chrisantha Fernando,et al. Pattern Recognition in a Bucket , 2003, ECAL.
[8] Michiel Hermans,et al. Optoelectronic Systems Trained With Backpropagation Through Time , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[9] Carla Teixeira Lopes,et al. TIMIT Acoustic-Phonetic Continuous Speech Corpus , 2012 .
[10] Daniel Brunner,et al. Parallel photonic information processing at gigabyte per second data rates using transient states , 2013, Nature Communications.
[11] Benjamin Schrauwen,et al. Optoelectronic Reservoir Computing , 2011, Scientific Reports.
[12] Geert Morthier,et al. Experimental demonstration of reservoir computing on a silicon photonics chip , 2014, Nature Communications.
[13] Razvan Pascanu,et al. Advances in optimizing recurrent networks , 2012, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[14] L. Larger,et al. Optoelectronic reservoir computing: tackling noise-induced performance degradation. , 2013, Optics express.
[15] Jan Danckaert,et al. Strongly asymmetric square waves in a time-delayed system. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[16] J.J. Steil,et al. Backpropagation-decorrelation: online recurrent learning with O(N) complexity , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[17] Geoffrey E. Hinton,et al. Generating Text with Recurrent Neural Networks , 2011, ICML.
[18] Laurent Larger,et al. Ikeda-based nonlinear delayed dynamics for application to secure optical transmission systems using chaos , 2004 .
[19] Lawrence K. Saul,et al. A fast online algorithm for large margin training of continuous density hidden Markov models , 2009, INTERSPEECH.
[20] Henry Markram,et al. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.
[21] Tamir Hazan,et al. PAC-Bayesian approach for minimization of phoneme error rate , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[22] Benjamin Schrauwen,et al. Toward optical signal processing using photonic reservoir computing. , 2008, Optics express.
[23] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[24] L Pesquera,et al. Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. , 2012, Optics express.
[25] Benjamin Schrauwen,et al. Phoneme Recognition with Large Hierarchical Reservoirs , 2010, NIPS.
[26] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[27] Benjamin Schrauwen,et al. Automated Design of Complex Dynamic Systems , 2014, PloS one.
[28] Yann LeCun,et al. Discriminative Recurrent Sparse Auto-Encoders , 2013, ICLR.
[29] Peter Tiño,et al. Minimum Complexity Echo State Network , 2011, IEEE Transactions on Neural Networks.
[30] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[31] Luca Maria Gambardella,et al. Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.
[32] Herbert Jaeger,et al. Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..
[33] Helmut Hauser,et al. Towards a theoretical foundation for morphological computation with compliant bodies , 2011, Biological Cybernetics.