Generic Deep Networks with Wavelet Scattering. - Edouard Oyallon; Stéphane Mallat; Laurent Sifre (ICLR (Workshop Poster), 2014)

Deep roto-translation scattering for object classification. - Edouard Oyallon; Stéphane Mallat (CVPR, 2015)

An Analysis of the SURF Method. - Edouard Oyallon; Julien Rabin (Image Process. Line, 2015)

Building a Regular Decision Boundary with Deep Networks. - Edouard Oyallon (CVPR, 2017)

Scaling the Scattering Transform: Deep Hybrid Networks. - Edouard Oyallon; Eugene Belilovsky; Sergey Zagoruyko (ICCV, 2017)

Multiscale Hierarchical Convolutional Networks. - Jörn-Henrik Jacobsen; Edouard Oyallon; Stéphane Mallat; Arnold W. M. Smeulders (CoRR, 2017)

Analyzing and Introducing Structures in Deep Convolutional Neural Networks. (Analyse et structuration des réseaux de neurones convolutifs profonds). - Edouard Oyallon (2017)

Compressing the Input for CNNs with the First-Order Scattering Transform. - Edouard Oyallon; Eugene Belilovsky; Sergey Zagoruyko; Michal Valko (ECCV (9), 2018)

i-RevNet: Deep Invertible Networks. - Jörn-Henrik Jacobsen; Arnold W. M. Smeulders; Edouard Oyallon (ICLR (Poster), 2018)

Nonlinear Acceleration of CNNs. - Damien Scieur; Edouard Oyallon; Alexandre d'Aspremont; Francis R. Bach (ICLR (Workshop), 2018)

Nonlinear Acceleration of Deep Neural Networks. - Damien Scieur; Edouard Oyallon; Alexandre d'Aspremont; Francis R. Bach (CoRR, 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; et al. (CoRR, 2018)

Greedy Layerwise Learning Can Scale To ImageNet. - Eugene Belilovsky; Michael Eickenberg; Edouard Oyallon (ICML, 2019)

On Lazy Training in Differentiable Programming. - Lénaïc Chizat; Edouard Oyallon; Francis R. Bach (NeurIPS, 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)

Decoupled Greedy Learning of CNNs. - Eugene Belilovsky; Michael Eickenberg; Edouard Oyallon (ICML, 2020)

Interferometric Graph Transform: a Deep Unsupervised Graph Representation. - Edouard Oyallon (ICML, 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; et al. (J. Mach. Learn. Res., 2020)

The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. - Louis Thiry; Michael Arbel; Eugene Belilovsky; Edouard Oyallon (ICLR, 2021)

Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment. - Othmane Laousy; Guillaume Chassagnon; Edouard Oyallon; Nikos Paragios; Marie-Pierre Revel; Maria Vakalopoulou (MLMI@MICCAI, 2021)

Interferometric Graph Transform for Community Labeling. - Nathan Grinsztajn; Louis Leconte; 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)

Low-Rank Projections of GCNs Laplacian. - Nathan Grinsztajn; Philippe Preux; Edouard Oyallon (CoRR, 2021)

Why do tree-based models still outperform deep learning on typical tabular data? - Léo Grinsztajn; Edouard Oyallon; Gaël Varoquaux (NeurIPS, 2022)

On Non-Linear operators for Geometric Deep Learning. - Grégoire Sergeant-Perthuis; Jakob Maier; Joan Bruna; Edouard Oyallon (NeurIPS, 2022)

Why do tree-based models still outperform deep learning on tabular data? - Léo Grinsztajn; Edouard Oyallon; Gaël Varoquaux (CoRR, 2022)

DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization for Time-Varying Gossips. - Adel Nabli; Edouard Oyallon (CoRR, 2022)

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)

Can Forward Gradient Match Backpropagation? - Louis Fournier; Stéphane Rivaud; Eugene Belilovsky; Michael Eickenberg; Edouard Oyallon (ICML, 2023)

DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization. - Adel Nabli; Edouard Oyallon (ICML, 2023)

Guiding The Last Layer in Federated Learning with Pre-Trained Models. - Gwen Legate; Nicolas Bernier; Lucas Page-Caccia; Edouard Oyallon; Eugene Belilovsky (NeurIPS, 2023)

A2CiD2: Accelerating Asynchronous Communication in Decentralized Deep Learning. - Adel Nabli; Eugene Belilovsky; Edouard Oyallon (NeurIPS, 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)

Gradient Masked Averaging for Federated Learning. - Irene Tenison; Sai Aravind Sreeramadas; Vaikkunth Mugunthan; Edouard Oyallon; Irina Rish; Eugene Belilovsky (Trans. Mach. Learn. Res., 2023)

Cyclic Data Parallelism for Efficient Parallelism of Deep Neural Networks. - Louis Fournier; Edouard Oyallon (CoRR, 2024)

WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average. - Louis Fournier; Adel Nabli; Masih Aminbeidokhti; Marco Pedersoli; Eugene Belilovsky; Edouard Oyallon (CoRR, 2024)

μLO: Compute-Efficient Meta-Generalization of Learned Optimizers. - Benjamin Thérien; Charles-Étienne Joseph; Boris Knyazev 001; Edouard Oyallon; Irina Rish; Eugene Belilovsky (CoRR, 2024)

ACCO: Accumulate while you Communicate, Hiding Communications in Distributed LLM Training. - Adel Nabli; Louis Fournier; Pierre Erbacher; Louis Serrano; Eugene Belilovsky; Edouard Oyallon (CoRR, 2024)

PETRA: Parallel End-to-end Training with Reversible Architectures. - Stéphane Rivaud; Louis Fournier; Thomas Pumir; Eugene Belilovsky; Michael Eickenberg; Edouard Oyallon (ICLR, 2025)

DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction. - Rudy Morel; Jiequn Han; Edouard Oyallon (CoRR, 2025)

Model Parallelism With Subnetwork Data Parallelism. - Vaibhav Singh; Zafir Khalid; Edouard Oyallon; Eugene Belilovsky (CoRR, 2025)