Thomas Mesnard
Thomas Mesnard
DeepMind
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Titre
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Année
Towards biologically plausible deep learning
Y Bengio, DH Lee, J Bornschein, T Mesnard, Z Lin
arXiv preprint arXiv:1502.04156, 2015
2332015
STDP-compatible approximation of backpropagation in an energy-based model
Y Bengio, T Mesnard, A Fischer, S Zhang, Y Wu
Neural computation 29 (3), 555-577, 2017
59*2017
An objective function for STDP
Y Bengio, T Mesnard, A Fischer, S Zhang, Y Wu
arXiv preprint arXiv:1509.05936, 2015
41*2015
Generalization of equilibrium propagation to vector field dynamics
B Scellier, A Goyal, J Binas, T Mesnard, Y Bengio
arXiv preprint arXiv:1808.04873, 2018
16*2018
Towards deep learning with spiking neurons in energy based models with contrastive hebbian plasticity
T Mesnard, W Gerstner, J Brea
arXiv preprint arXiv:1612.03214, 2016
112016
Hindsight credit assignment
A Harutyunyan, W Dabney, T Mesnard, MG Azar, B Piot, N Heess, ...
Advances in neural information processing systems, 12488-12497, 2019
62019
From STDP towards Biologically Plausible Deep Learning
Y Bengio, A Fischer, T Mesnard, S Zhang, Y Wu
ICML 2015, Deep Learning Workshop, 2015
42015
Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks
T Mesnard, G Vignoud, J Sacramento, W Senn, Y Bengio
arXiv preprint arXiv:1911.08585, 2019
12019
Activation alignment: exploring the use of approximate activity gradients in multilayer networks
T Mesnard, B Richards
Cognitive Computational Neuroscience, 2018
2018
Fully discretized training of neural networks through direct feedback
T Mesnard, G Vignoud, J Binas, Y Bengio
2018
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