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ORCIDMathieu Galtier
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2020 – today
- 2024
[j10]Wouter Heyndrickx
, Lewis H. Mervin, Tobias Morawietz, Noé Sturm, Lukas Friedrich, Adam Zalewski, Anastasia Pentina, Lina Humbeck, Martijn Oldenhof, Ritsuya Niwayama, Peter Schmidtke, Nikolas Fechner, Jaak Simm, Adam Arany, Nicolas Drizard, Rama Jabal, Arina Afanasyeva, Regis Loeb, Shlok Verma, Simon Harnqvist, Matthew Holmes, Balazs Pejo, Maria Telenczuk, Nicholas Holway, Arne Dieckmann, Nicola Rieke, Friederike Zumsande, Djork-Arné Clevert, Michael Krug, Christopher N. Luscombe, Darren V. S. Green, Peter Ertl, Peter Antal, David Marcus, Nicolas Do Huu, Hideyoshi Fuji, Stephen D. Pickett, Gergely Ács, Eric Boniface, Bernd Beck, Yax Sun, Arnaud Gohier, Friedrich Rippmann, Ola Engkvist, Andreas H. Göller, Yves Moreau, Mathieu N. Galtier, Ansgar Schuffenhauer, Hugo Ceulemans:
MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information. J. Chem. Inf. Model. 64(7): 2331-2344 (2024)- 2023
- [c2]Martijn Oldenhof, Gergely Ács, Balázs Pejó, Ansgar Schuffenhauer, Nicholas Holway, Noé Sturm, Arne Dieckmann, Oliver Fortmeier, Eric Boniface, Clément Mayer, Arnaud Gohier, Peter Schmidtke, Ritsuya Niwayama, Dieter Kopecky, Lewis H. Mervin, Prakash Chandra Rathi, Lukas Friedrich, András Formanek, Peter Antal, Jordon Rahaman, Adam Zalewski, Wouter Heyndrickx, Ezron Oluoch, Manuel Stößel, Michal Vanco, David Endico, Fabien Gelus, Thaïs de Boisfossé, Adrien Darbier, Ashley Nicollet, Matthieu Blottière, Maria Telenczuk, Van Tien Nguyen, Thibaud Martinez, Camille Boillet, Kelvin Moutet, Alexandre Picosson, Aurélien Gasser, Inal Djafar, Antoine Simon, Adam Arany, Jaak Simm, Yves Moreau, Ola Engkvist, Hugo Ceulemans, Camille Marini, Mathieu Galtier:
Industry-Scale Orchestrated Federated Learning for Drug Discovery. AAAI 2023: 15576-15584 - 2022
- [j9]Balazs Pejo, Mina Remeli, Adam Arany, Mathieu Galtier, Gergely Ács:
Collaborative Drug Discovery: Inference-level Data Protection Perspective. Trans. Data Priv. 15(2): 87-107 (2022) - [i7]Balazs Pejo, Mina Remeli, Adam Arany, Mathieu Galtier, Gergely Ács:
Collaborative Drug Discovery: Inference-level Data Protection Perspective. CoRR abs/2205.06506 (2022) - [i6]Martijn Oldenhof, Gergely Ács, Balázs Pejó, Ansgar Schuffenhauer, Nicholas Holway, Noé Sturm, Arne Dieckmann, Oliver Fortmeier, Eric Boniface, Clément Mayer, Arnaud Gohier, Peter Schmidtke, Ritsuya Niwayama, Dieter Kopecky, Lewis H. Mervin, Prakash Chandra Rathi, Lukas Friedrich, András Formanek, Peter Antal, Jordon Rahaman, Adam Zalewski, Ezron Oluoch, Manuel Stößel, Michal Vanco, David Endico, Fabien Gelus, Thaïs de Boisfossé, Adrien Darbier, Ashley Nicollet, Matthieu Blottière, Maria Telenczuk, Van Tien Nguyen, Thibaud Martinez, Camille Boillet, Kelvin Moutet, Alexandre Picosson, Aurélien Gasser, Inal Djafar, Adam Arany, Jaak Simm, Yves Moreau, Ola Engkvist, Hugo Ceulemans, Camille Marini, Mathieu Galtier:
Industry-Scale Orchestrated Federated Learning for Drug Discovery. CoRR abs/2210.08871 (2022) - 2020
- [j8]Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletarì, Holger R. Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus H. Maier-Hein, Sébastien Ourselin, Micah J. Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso:
The future of digital health with federated learning. npj Digit. Medicine 3 (2020) - [i5]Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus H. Maier-Hein, Sébastien Ourselin, Micah J. Sheller, Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust, M. Jorge Cardoso:
The Future of Digital Health with Federated Learning. CoRR abs/2003.08119 (2020)
2010 – 2019
- 2019
- [i4]Mathieu N. Galtier, Camille Marini:
Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning. CoRR abs/1910.11567 (2019) - 2018
- [j7]Tomasz Górski, Romain Veltz, Mathieu Galtier, Hélissande Fragnaud, Jennifer S. Goldman, Bartosz Telenczuk, Alain Destexhe:
Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity. J. Comput. Neurosci. 45(3): 223-234 (2018) - [c1]Stanislas Chambon, Mathieu N. Galtier, Alexandre Gramfort:
Domain adaptation with optimal transport improves EEG sleep stage classifiers. PRNI 2018: 1-4 - 2017
- [i3]Mathieu Galtier, Camille Marini:
Morpheo: Traceable Machine Learning on Hidden data. CoRR abs/1704.05017 (2017) - [i2]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
- [j6]Guillaume Bellec, Mathieu Galtier, Romain Brette, Pierre Yger:
Slow feature analysis with spiking neurons and its application to audio stimuli. J. Comput. Neurosci. 40(3): 317-329 (2016) - [j5]Gilles Wainrib, Mathieu Galtier:
A local Echo State Property through the largest Lyapunov exponent. Neural Networks 76: 39-45 (2016) - 2015
- [j4]Mathieu Galtier:
Ideomotor feedback control in a recurrent neural network. Biol. Cybern. 109(3): 363-375 (2015) - 2014
- [j3]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
- [j2]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
- [j1]Mathieu Galtier, Olivier D. Faugeras, Paul C. Bressloff:
Hebbian Learning of Recurrent Connections: A Geometrical Perspective. Neural Comput. 24(9): 2346-2383 (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
- [b1]Mathieu Galtier:
A mathematical approach to unsupervised learning in recurrent neural networks. (Une approche mathématique de l'apprentissage non-supervisé dans les réseaux de neurones récurrents). Mines ParisTech, France, 2011
Coauthor Index
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