MSc in Applied Mathematics, Machine Learning, Computer Vision (a.k.a Master MVA).
École Normale Supérieure, Paris. Completed summa cum laude.
Research Internship at the Laboratory of Computational Neuroscience, EPFL
Supervisors: Johanni Brea and Wulfram Gerstner
→ Exploring deep learning techniques with spiking neurons in energy based models
→ Investigating backpropagation with random feedback weights in deep neural networks
Research Internship at the Montreal Institute for Learning Algorithms, University of Montreal
Supervisor: Yoshua Bengio
→ Focusing on new biologically plausible deep learning algorithms
→ Exploring new versions of RNNs/Clockwork RNNs using LSTM and GRU
Research Internship at the Laboratory of Neuronal Circuit Development,Institut Curie
Supervisors: Christoph Gebhardt and Filippo Del Bene
→ ZebraFish visual system mapping using data-analysis and machine learning
I am interested in deep learning, unsupervised Learning and neuroscience.
“STDP-Compatible Approximation of Backpropagation in an Energy-Based Model”. Neural Computation. 2017.
Yoshua Bengio, Thomas Mesnard, Asja Fischer, Saizheng Zhang, and Yuhuai Wu.
”Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity”. NIPS. Computing with Spikes Workshop. 2016. With Oral Presentation and Poster.
Thomas Mesnard, Wulfram Gerstner, and Johanni Brea.
“From STDP towards Biologically Plausible Deep Learning”. ICML. Deep Learning Workshop. 2015.
Yoshua Bengio, Asja Fischer, Thomas Mesnard, Saizheng Zhang, and Yuhuai Wu.
“Towards biologically plausible deep learning”. ArXiv. Preprint:1502.04156v3. 2015.
Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, and Zhouhan Lin.
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