I am Edouard Oyallon, a member of the *Centre de Vision Numérique (CVN) de CentraleSupelec*. Prior to it, I was a postdoctoral research fellow at the *INRIA Lille*, where I worked in particular with Michal Valko. I obtained my PhD in October 2017 from the *Département Informatique de l'Ecole Normale Supérieure* under the supervision of Prof. Stéphane Mallat. I graduated from the *ENS Cachan, campus de Ker Lann*. My current main research topic is to understand the theoritical fundations of feature engineering methods.

I hate black box pipelines, and to be concise, the type of questions I try to solve is not “how to learn new deep features that discriminate this image as a dog”, but simply “why is your network understanding that this signal is a dog”. Answering such questions could help to make fundamental advances in science in a context where too much work is dedicated to techniques that give incremental improvements on standard datasets.

Feel free to send me any emails to discuss my work at __edouard[dot]oyallon[at]centralesupelec[dot]fr__.

**News:**
Feb 2018, I am grateful to have received a GPU donation from NVIDIA.

Jan 1st 2018, I will join the CVN at CentraleSupelec.

Nov 1st 2017, I will join the SequeL team at Lille, to work in particular with Michal Valko and Matteo Pirotta.

My GitHub.

- Belilovsky, E., Eickenberg M.,
**Oyallon, E.**-*Decoupled Greedy Learning of CNNs*, preprint. https://arxiv.org/abs/1901.08164 - Andreux M., Angles T., Exarchakis G., Leonarduzzi R., Rochette G., Thiry L., Zarka J., Mallat S., Andén J., Belilovsky E., Bruna J., Lostanlen V., Matthew J. Hirn,
**Oyallon E.**, Zhang S., Cella C., Eickenberg, M. -*Kymatio: Scattering Transforms in Python*, preprint. https://arxiv.org/abs/1812.11214 - Scieur, D.,
**Oyallon, E.**, d'Aspremont, A. and Bach, F. -*Nonlinear Acceleration of Deep Neural Networks*, preprint. https://arxiv.org/abs/1805.09639 - Belilovsky, E., Eickenberg M.,
**Oyallon, E.**-*Greedy Layerwise Learning Can Scale to ImageNet*, ICML 2019. https://arxiv.org/abs/1812.11446 - Belilovsky, E., Eickenberg M.,
**Oyallon, E.**-*Do Deep Convolutional Network Layers Need to be Trained End-to-End?*, NIPS CRACT workshop 2018. **Oyallon, E.**, Belilovsky, E., Zagoruyko, S., and Valko, M. -*Compressing the Input for CNNs with the First-Order scattering Transform*, ECCV 2018. https://arxiv.org/abs/1809.10200, poster- Scieur, D.,
**Oyallon, E.**, d'Aspremont, A. and Bach, F. -*Nonlinear Acceleration of CNNs*, ICLR workshop 2018. https://openreview.net/forum?id=HkNpF_kDM **Oyallon, E.**, Zagoruyko, S., Huang G., Komodakis, N., Lacoste-Julien, S., Blaschko M., and Belilovsky E. -*Scattering Networks for Hybrid Representation Learning*, TPAMI 2018. https://arxiv.org/abs/1809.06367- Jacobsen, J.-H., Smeulders, A.W.M. and
**Oyallon, E.**-*i-RevNet: Deep Invertible Networks*, ICLR 2018. https://openreview.net/forum?id=HJsjkMb0Z **Oyallon, E.**-*Analyzing and Introducing Structures in Deep Convolutional Neural Networks*, "Thèse de doctorat", 2017, slides**Oyallon, E.**, Belilovsky, E., and Zagoruyko, S. -*Scaling the Scattering Transform: Deep Hybrid Networks*, ICCV 2017. https://arxiv.org/abs/1703.08961, poster- Jacobsen, J.-H.,
**Oyallon, E.**, Mallat, S. and Smeulders, A.W.M. -*Multiscale Hierarchical Convolutional Networks*, ICML PADL 2017. https://arxiv.org/abs/1703.04140, poster **Oyallon, E.**-*Building a Regular Decision Boundary with Deep Networks*, CVPR 2017. https://arxiv.org/abs/1703.01775, poster**Oyallon, E.**-*A hybrid network: Scattering and Convnet*. https://openreview.net/pdf?id=ryPx38qge, reviews**Oyallon, E.**and Mallat, S. -*Deep Roto-translation Scattering for Object Classification*, CVPR 2015. http://arxiv.org/abs/1412.8659, poster**Oyallon, E.**and Rabin, J. -*An Analysis of the SURF Method*, IPOL 2015. http://www.ipol.im/pub/art/2015/69/**Oyallon, E.**, Mallat, S. and Sifre, L. -*Generic Deep Networks with wavelet Scattering*, ICLR 2014 workshop. http://arxiv.org/abs/1312.5940, poster

- NIPS 2017 Best Reviewer Award
- ICCV17 Student Volunteer Awards
- ICLR 2017 Best Review Award
- PhD grant, DIM-RDM

- 2019, Co-organizer of the ICLR 2019 Workshop:
*Learning with Limited Labeled Data: Representation Learning for Weak Supervision and Beyond* - 2019, Reviewer @ ICLR
- 2018, Lecturer at
*Ateliers Statistiques de la Société Française de Statistique* - 2018, Reviewer @ ICLR, ICML, CVPR
- 2017, Co-organizer of the NIPS 2017 Workshop:
*Learning with Limited Labeled Data: Weak Supervision and Beyond* - 2017, Reviewer @ ICLR, NIPS
- 2016, Reviewer @ IPOL

- 2019, Lecturer, Reinforcement Learning, CentraleSupélec
- 2019, Lecturer, Deep Learning in Practice, MVA/CentraleSupélec, with Guillaume Charpiat, webpage of the class
- 2018, Teaching assistant, Deep Learning, MVA
- 2018, Second year seminar ENSAE, slides
- 2017, Corporate Seminar Series with lumenai
- 2017, Seminar at M2 StatML, slides
- 2017, Second year seminar ENSAE, slides
- 2014-2017, Teaching assistant, ENSAE, fundamental probability classes and calculus

- 2019, Journee Stat/ML de Paris-Saclay, IHES
- 2019, Séminaire Parisien de Statistique, IHP
- 2018, GT DeepNet, LRI/TAO, Gif-sur-Yvette
- 2018, GREYC, Caen
- 2018, GE Healthcare, Bures-sur-Yvette
- 2018, NAVER LABS, Grenoble
- 2018, Criteo, Paris
- 2018, SequeL, Lille
- 2018, SONY CSL Music
- 2018, DeepMind CSML Seminar Series
- 2018, Mathematical coffee at Huawei
- 2018, Imaging in Paris Seminar, IHP
- 2017, GREYC, Caen
- 2017, LIP6, Paris
- 2017, cfm
- 2017, Torr Vision Group
- 2017, Paris Big Data, Télécom Paris Tech, slides.
- 2017, Leuven, seminar.
- 2017, "Groupe de travail", about deep learning, Rennes 1, invited by Adrien Saumard, slides.
- 2017, Meetup at Rennes, France.
- 2016, Meetup at Pau, France.
- 2015, Journée DIM RDM-IDF, UPMC, Paris, talk (french).
- 2015, GREYC, Paris, slides.

- Kymatio, http://www.kymat.io. Wavelet Scattering Transforms in Python with GPU acceleration.
- Scattering Networks in PyTorch, PyScatWave (2017) (ScatWave2.0 (2017), Lua Torch version). Pipelines for imagenet: Scaling Scattering (2017).

Collaboration with Eugene Belilovsky and Sergey Zagoruyko - Multiscale Hiearchical CNN (2017), software in TensorFlow and Keras

Collaboration with Jörn-Henrik Jacobsen - Deep Separation Contraction (2017), software in TensorFlow
- ScatWave (2016), software for Torch
- ScatNet (2013), ScatNetLight (2015), softwares for MATLAB