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Random pattern and frequency generation using a photonic reservoir computer with output feedback

Dec 19, 2020
Piotr Antonik, Michiel Hermans, Marc Haelterman, Serge Massar

* Neural Processing Letters (Volume: 47, Pages: 1041-1054, 13 April 2017) 
* 15 pages, 9 figures. arXiv admin note: substantial text overlap with arXiv:1802.02026 

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A Differentiable Physics Engine for Deep Learning in Robotics

Nov 24, 2018
Jonas Degrave, Michiel Hermans, Joni Dambre, Francis wyffels

* Submitted for International Conference on Learning Representations 2017 

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Embodiment of Learning in Electro-Optical Signal Processors

Oct 27, 2016
Michiel Hermans, Piotr Antonik, Marc Haelterman, Serge Massar

* Physical Review Letters 117, 128301 (2016) 
* Main text (5 pages, 2 figures) merged with the supplementary material (8 pages, 5 figures) 

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Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation

Oct 20, 2016
Piotr Antonik, François Duport, Michiel Hermans, Anteo Smerieri, Marc Haelterman, Serge Massar

* IEEE Transactions on Neural Networks and Learning Systems ( Volume: 28, Issue: 11, Nov. 2017 ) 
* 13 pages, 10 figures 

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Towards Trainable Media: Using Waves for Neural Network-Style Training

Sep 30, 2015
Michiel Hermans, Thomas Van Vaerenbergh

* submitted to Scientific Reports 

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Photonic Delay Systems as Machine Learning Implementations

Jan 12, 2015
Michiel Hermans, Miguel Soriano, Joni Dambre, Peter Bienstman, Ingo Fischer

* Journal of Machine Learning Research, vol. 16, pp. 2081-2097 (2015) 

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Trainable and Dynamic Computing: Error Backpropagation through Physical Media

Jul 24, 2014
Michiel Hermans, Michaël Burm, Joni Dambre, Peter Bienstman

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Memristor models for machine learning

Jul 14, 2014
Juan Pablo Carbajal, Joni Dambre, Michiel Hermans, Benjamin Schrauwen

* 4 figures, no tables. Submitted to neural computation 

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