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Gilles Wainrib
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2010 – 2019
- 2018
- [c3]Jonathan Dong, Sylvain Gigan, Florent Krzakala, Gilles Wainrib:
Scaling Up Echo-State Networks With Multiple Light Scattering. SSP 2018: 448-452 - [i6]Pierre Courtiol, Eric W. Tramel, Marc Sanselme, Gilles Wainrib:
Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach. CoRR abs/1802.02212 (2018) - [i5]Mikhail Zaslavskiy, Simon Jégou, Eric W. Tramel, Gilles Wainrib:
ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors. CoRR abs/1806.04449 (2018) - 2017
- [i4]Stanislas Chambon, Mathieu Galtier, Pierrick Arnal, Gilles Wainrib, Alexandre Gramfort:
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. CoRR abs/1707.03321 (2017) - 2016
- [j8]Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali:
The Asymptotic Performance of Linear Echo State Neural Networks. J. Mach. Learn. Res. 17: 178:1-178:35 (2016) - [j7]Gilles Wainrib, Mathieu Galtier:
A local Echo State Property through the largest Lyapunov exponent. Neural Networks 76: 39-45 (2016) - [j6]Romain Couillet, Gilles Wainrib:
Perspectives en matrices aléatoires et grands réseaux. Traitement du Signal 33(2-3): 351-376 (2016) - [c2]Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi:
A Random Matrix Approach to Echo-State Neural Networks. ICML 2016: 517-525 - [c1]Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali:
Training performance of echo state neural networks. SSP 2016: 1-4 - [i3]Romain Couillet, Gilles Wainrib, Harry Sevi, Hafiz Tiomoko Ali:
The Asymptotic Performance of Linear Echo State Neural Networks. CoRR abs/1603.07866 (2016) - [i2]Jonathan Dong, Sylvain Gigan, Florent Krzakala, Gilles Wainrib:
Scaling up Echo-State Networks with multiple light scattering. CoRR abs/1609.05204 (2016) - 2014
- [j5]Mathieu Galtier, Camille Marini, Gilles Wainrib, Herbert Jaeger:
Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes. Neural Networks 56: 10-21 (2014) - 2013
- [j4]Mathieu Galtier, Gilles Wainrib:
A Biological Gradient Descent for Prediction Through a Combination of STDP and Homeostatic Plasticity. Neural Comput. 25(11): 2815-2832 (2013) - 2012
- [j3]Gilles Wainrib, Michèle Thieullen, Khashayar Pakdaman:
Reduction of stochastic conductance-based neuron models with time-scales separation. J. Comput. Neurosci. 32(2): 327-346 (2012) - [i1]Mathieu Galtier, Gilles Wainrib:
A combination of STDP, Hebbian learning and synaptic scaling deriving from a theoretical learning principle. CoRR abs/1206.4812 (2012) - 2011
- [j2]Gilles Wainrib, Michèle Thieullen, Khashayar Pakdaman:
Erratum to: Intrinsic variability of latency to first-spike. Biol. Cybern. 105(3-4): 285 (2011) - 2010
- [j1]Gilles Wainrib, Michèle Thieullen, Khashayar Pakdaman:
Intrinsic variability of latency to first-spike. Biol. Cybern. 103(1): 43-56 (2010)
Coauthor Index
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