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Michiel Hermans

Showing results (1-10 of 11) with videos related to

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Neural Computation|August 20, 2011
Recurrent kernel machines: computing with infinite echo state networksMichiel Hermans, Benjamin Schrauwen
Neural Networks : the Official Journal of the International Neural Network Society|September 15, 2009
Memory in linear recurrent neural networks in continuous timeMichiel Hermans, Benjamin Schrauwen
IEEE Transactions on Neural Networks and Learning Systems|August 20, 2014
Optoelectronic Systems Trained With Backpropagation Through TimeMichiel Hermans, Joni Dambre, Peter Bienstman
Neural Computation|April 2, 2014
Toward unified hybrid simulation techniques for spiking neural networksMichiel D'Haene, Michiel Hermans, Benjamin Schrauwen
Frontiers in Computational Neuroscience|July 27, 2013
MACOP modular architecture with control primitivesTim Waegeman, Michiel Hermans, Benjamin Schrauwen
Plos One|February 6, 2014
Automated design of complex dynamic systemsMichiel Hermans, Benjamin Schrauwen, Peter Bienstman, et al.
Physical Review Letters|October 1, 2016
Embodiment of Learning in Electro-Optical Signal ProcessorsMichiel Hermans, Piotr Antonik, Marc Haelterman, et al.
Frontiers in Neurorobotics|March 23, 2019
A Differentiable Physics Engine for Deep Learning in RoboticsJonas Degrave, Michiel Hermans, Joni Dambre, et al.
Neural Computation|January 21, 2015
Memristor models for machine learningJuan Pablo Carbajal, Joni Dambre, Michiel Hermans, et al.
Nature Communications|March 25, 2015
Trainable hardware for dynamical computing using error backpropagation through physical mediaMichiel Hermans, Michaël Burm, Thomas Van Vaerenbergh, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Neural Computation|August 20, 2011
Recurrent kernel machines: computing with infinite echo state networksMichiel Hermans, Benjamin Schrauwen
Neural Networks : the Official Journal of the International Neural Network Society|September 15, 2009
Memory in linear recurrent neural networks in continuous timeMichiel Hermans, Benjamin Schrauwen
IEEE Transactions on Neural Networks and Learning Systems|August 20, 2014
Optoelectronic Systems Trained With Backpropagation Through TimeMichiel Hermans, Joni Dambre, Peter Bienstman
Neural Computation|April 2, 2014
Toward unified hybrid simulation techniques for spiking neural networksMichiel D'Haene, Michiel Hermans, Benjamin Schrauwen
Frontiers in Computational Neuroscience|July 27, 2013
MACOP modular architecture with control primitivesTim Waegeman, Michiel Hermans, Benjamin Schrauwen
Plos One|February 6, 2014
Automated design of complex dynamic systemsMichiel Hermans, Benjamin Schrauwen, Peter Bienstman, et al.
Physical Review Letters|October 1, 2016
Embodiment of Learning in Electro-Optical Signal ProcessorsMichiel Hermans, Piotr Antonik, Marc Haelterman, et al.
Frontiers in Neurorobotics|March 23, 2019
A Differentiable Physics Engine for Deep Learning in RoboticsJonas Degrave, Michiel Hermans, Joni Dambre, et al.
Neural Computation|January 21, 2015
Memristor models for machine learningJuan Pablo Carbajal, Joni Dambre, Michiel Hermans, et al.
Nature Communications|March 25, 2015
Trainable hardware for dynamical computing using error backpropagation through physical mediaMichiel Hermans, Michaël Burm, Thomas Van Vaerenbergh, et al.
Pageof 2