Thomas Mesnard
Research Assistant at DeepMind and PhD student at École Polytechnique.
Research Interests
I am interested in reinforcement learning, unsupervised Learning and neuroscience.
Publications
2019 “Hindsight Credit Assignment” by Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado P van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup and Remi Munos. In: NeurIPS 2019. pdf
2018 “Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks” by Thomas Mesnard, Gaëtan Vignoud, Joao Sacramento, Walter Senn and Yoshua Bengio. In: CCN 2018. pdf
2018 “Generalization of Equilibrium Propagation to Vector Field Dynamics” by Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, and Yoshua Bengio. In: ICLR 2018. Workshop. pdf
2017 “STDP-Compatible Approximation of Backpropagation in an Energy-Based Model” by Yoshua Bengio, Thomas Mesnard, Asja Fischer, Saizheng Zhang, and Yuhuai Wu. In: Neural computation. pdf
2016 “Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity” by Thomas Mesnard, Wulfram Gerstner, and Johanni Brea. In: NIPS 2016. Computing with Spikes Workshop. pdf poster
2015 “Towards biologically plausible deep learning” by Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, and Zhouhan Lin. In: arXiv. pdf
Education
PhD student in Applied Mathematics and Computer Science.
2019 - Present
École Polytechnique
MSc in Applied Mathematics, Machine Learning, Computer Vision (a.k.a Master MVA).
2016 - 2017
Computer Science Department, École Normale Supérieure, Paris. Completed with honors.
Supervisor: Francis Bach
Work Experience
Research Assistant at DeepMind, Paris, France.
2019 - Present
Research Internship at DeepMind, London, UK.
2018 - 2019 - 5 months
Supervisor: Rémi Munos
Research Internship at the Montreal Institute for Learning Algorithms, University of Montreal
2017 - 2018 - 1 year
Supervisors: Blake Richards and Yoshua Bengio
Research Internship at the Laboratory of Computational Neuroscience, EPFL
2016 - 4 months
Supervisors: Johanni Brea and Wulfram Gerstner
Research Internship at the Montreal Institute for Learning Algorithms, University of Montreal
2015 - 5 months
Supervisor: Yoshua Bengio
Implementations
Teaching Machines to Read and Comprehend by Karl Moritz Hermann and al. from Google DeepMind. Code. Report.
With Alex Auvolat and Étienne Simon
CTC: Labelling Unsegmented
Sequence Data with RNN by Alex Graves and al. from Google DeepMind. Code. Report. Poster.
With Alex Auvolat
Predictive models for transaction volumes in financial markets in response to a competition proposed by Capital Fund Management. Code. Report.
Associated course : Sparse Wavelet Representations and Classification by Stéphane Mallat