Edouard Oyallon

According to our database, Edouard Oyallon authored at least 35 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Cyclic Data Parallelism for Efficient Parallelism of Deep Neural Networks.

Louis Fournier, Edouard Oyallon

CoRR, 2024

2023

Vectorizing string entries for data processing on tables: when are larger language models better?

Léo Grinsztajn, Edouard Oyallon, Myung Jun Kim, Gaël Varoquaux

CoRR, 2023

A²CiD²: Accelerating Asynchronous Communication in Decentralized Deep Learning.

Adel Nabli, Eugene Belilovsky, Edouard Oyallon

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Guiding The Last Layer in Federated Learning with Pre-Trained Models.

Gwen Legate, Nicolas Bernier, Lucas Page-Caccia, Edouard Oyallon, Eugene Belilovsky

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization.

Adel Nabli, Edouard Oyallon

Proceedings of the International Conference on Machine Learning, 2023

Can Forward Gradient Match Backpropagation?

Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon

Proceedings of the International Conference on Machine Learning, 2023

Contributions to Local, Asynchronous and Decentralized Learning, and to Geometric Deep Learning. (Contributions à l'apprentissage local, asynchrone et décentralisé, et à l'apprentissage profond sur variété).

Edouard Oyallon

, 2023

2022

DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization for Time-Varying Gossips.

Adel Nabli, Edouard Oyallon

CoRR, 2022

Why do tree-based models still outperform deep learning on tabular data?

Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux

CoRR, 2022

Gradient Masked Averaging for Federated Learning.

Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Eugene Belilovsky, Irina Rish

CoRR, 2022

On Non-Linear operators for Geometric Deep Learning.

Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Why do tree-based models still outperform deep learning on typical tabular data?

Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Low-Rank Projections of GCNs Laplacian.

Nathan Grinsztajn, Philippe Preux, Edouard Oyallon

CoRR, 2021

Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning.

Eugene Belilovsky, Louis Leconte, Lucas Caccia, Michael Eickenberg, Edouard Oyallon

CoRR, 2021

Interferometric Graph Transform for Community Labeling.

Nathan Grinsztajn, Louis Leconte, Philippe Preux, Edouard Oyallon

CoRR, 2021

Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment.

Othmane Laousy, Guillaume Chassagnon, Edouard Oyallon, Nikos Paragios, Marie-Pierre Revel, Maria Vakalopoulou

Proceedings of the Machine Learning in Medical Imaging - 12th International Workshop, 2021

The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods.

Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Kymatio: Scattering Transforms in Python.

Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine-Emanuele Cella, Michael Eickenberg

J. Mach. Learn. Res., 2020

Interferometric Graph Transform: a Deep Unsupervised Graph Representation.

Edouard Oyallon

Proceedings of the 37th International Conference on Machine Learning, 2020

Decoupled Greedy Learning of CNNs.

Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Scattering Networks for Hybrid Representation Learning.

Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky

IEEE Trans. Pattern Anal. Mach. Intell., 2019

On Lazy Training in Differentiable Programming.

Lénaïc Chizat, Edouard Oyallon, Francis R. Bach

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Greedy Layerwise Learning Can Scale To ImageNet.

Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Kymatio: Scattering Transforms in Python.

Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine-Emanuele Cella, Michael Eickenberg

CoRR, 2018

Nonlinear Acceleration of Deep Neural Networks.

Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach

CoRR, 2018

Nonlinear Acceleration of CNNs.

Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach

Proceedings of the 6th International Conference on Learning Representations, 2018

i-RevNet: Deep Invertible Networks.

Jörn-Henrik Jacobsen, Arnold W. M. Smeulders, Edouard Oyallon

Proceedings of the 6th International Conference on Learning Representations, 2018

Compressing the Input for CNNs with the First-Order Scattering Transform.

Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko

Proceedings of the Computer Vision - ECCV 2018, 2018

2017

Analyzing and Introducing Structures in Deep Convolutional Neural Networks. (Analyse et structuration des réseaux de neurones convolutifs profonds).

Edouard Oyallon

PhD thesis, 2017

Multiscale Hierarchical Convolutional Networks.

Jörn-Henrik Jacobsen, Edouard Oyallon, Stéphane Mallat, Arnold W. M. Smeulders

CoRR, 2017

Scaling the Scattering Transform: Deep Hybrid Networks.

Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko

Proceedings of the IEEE International Conference on Computer Vision, 2017

Building a Regular Decision Boundary with Deep Networks.

Edouard Oyallon

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2015

An Analysis of the SURF Method.

Edouard Oyallon, Julien Rabin

Image Process. Line, 2015

Deep roto-translation scattering for object classification.

Edouard Oyallon, Stéphane Mallat

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014

Generic Deep Networks with Wavelet Scattering.

Edouard Oyallon, Stéphane Mallat, Laurent Sifre

Proceedings of the 2nd International Conference on Learning Representations, 2014