Michiel Hermans

According to our database, Michiel Hermans authored at least 23 papers between 2010 and 2019.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2019

A Differentiable Physics Engine for Deep Learning in Robotics.

Jonas Degrave, Michiel Hermans, Joni Dambre, Francis Wyffels

Frontiers Neurorobotics, 2019

2018

Random Pattern and Frequency Generation Using a Photonic Reservoir Computer with Output Feedback.

Piotr Antonik, Michiel Hermans, Marc Haelterman, Serge Massar

Neural Process. Lett., 2018

2017

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

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

IEEE Trans. Neural Networks Learn. Syst., 2017

Photonic reservoir computer with output feedback for chaotic time series prediction.

Piotr Antonik, Michiel Hermans, Marc Haelterman, Serge Massar

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Chaotic Time Series Prediction Using a Photonic Reservoir Computer with Output Feedback.

Piotr Antonik, Michiel Hermans, Marc Haelterman, Serge Massar

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Embodiment of Learning in Electro-Optical Signal Processors.

Michiel Hermans, Piotr Antonik, Marc Haelterman, Serge Massar

CoRR, 2016

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

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

CoRR, 2016

Pattern and Frequency Generation Using an Opto-Electronic Reservoir Computer with Output Feedback.

Piotr Antonik, Michiel Hermans, Marc Haelterman, Serge Massar

Proceedings of the Neural Information Processing - 23rd International Conference, 2016

Towards Adjustable Signal Generation with Photonic Reservoir Computers.

Piotr Antonik, Michiel Hermans, Marc Haelterman, Serge Massar

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

2015

Optoelectronic Systems Trained With Backpropagation Through Time.

Michiel Hermans, Joni Dambre, Peter Bienstman

IEEE Trans. Neural Networks Learn. Syst., 2015

Memristor Models for Machine Learning.

Juan Pablo Carbajal, Joni Dambre, Michiel Hermans, Benjamin Schrauwen

Neural Comput., 2015

Photonic delay systems as machine learning implementations.

Michiel Hermans, Miguel C. Soriano, Joni Dambre, Peter Bienstman, Ingo Fischer

J. Mach. Learn. Res., 2015

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

Michiel Hermans, Thomas Van Vaerenbergh

CoRR, 2015

Online Training of an Opto-Electronic Reservoir Computer.

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

Proceedings of the Neural Information Processing - 22nd International Conference, 2015

2014

Toward Unified Hybrid Simulation Techniques for Spiking Neural Networks.

Michiel D'Haene, Michiel Hermans, Benjamin Schrauwen

Neural Comput., 2014

Trainable and Dynamic Computing: Error Backpropagation through Physical Media.

Michiel Hermans, Michael Burm, Joni Dambre, Peter Bienstman

CoRR, 2014

2013

MACOP modular architecture with control primitives.

Tim Waegeman, Michiel Hermans, Benjamin Schrauwen

Frontiers Comput. Neurosci., 2013

Training and Analysing Deep Recurrent Neural Networks.

Michiel Hermans, Benjamin Schrauwen

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Recurrent Kernel Machines: Computing with Infinite Echo State Networks.

Michiel Hermans, Benjamin Schrauwen

Neural Comput., 2012

Infinite Sparse Threshold Unit Networks.

Michiel Hermans, Benjamin Schrauwen

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

2010

Memory in linear recurrent neural networks in continuous time.

Michiel Hermans, Benjamin Schrauwen

Neural Networks, 2010

One step Backpropagation Through Time for learning input mapping in reservoir computing applied to speech recognition.

Michiel Hermans, Benjamin Schrauwen

Proceedings of the International Symposium on Circuits and Systems (ISCAS 2010), May 30, 2010

Memory in reservoirs for high dimensional input.

Michiel Hermans, Benjamin Schrauwen

Proceedings of the International Joint Conference on Neural Networks, 2010