Edouard Oyallon

64 papers A* 16Journal 41Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Stabilizing Native Low-Rank LLM Pretraining. CoRR Paul Janson, Edouard Oyallon, Eugene Belilovsky
2026	J	`jnl`	Test-time Generalization for Physics through Neural Operator Splitting. CoRR Louis Serrano, Jiequn Han, Edouard Oyallon, Shirley Ho, Rudy Morel
2026	J	`jnl`	Unbiased Approximate Vector-Jacobian Products for Efficient Backpropagation. CoRR Killian Bakong, Laurent Massoulié, Edouard Oyallon, Kevin Scaman
2025	A*	`conf`	DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction. ICML Rudy Morel, Jiequn Han, Edouard Oyallon
2025	J	`jnl`	DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction. CoRR Rudy Morel, Jiequn Han, Edouard Oyallon
2025	J	`jnl`	Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration. J. Mach. Learn. Res. Adel Nabli, Edouard Oyallon
2025	J	`jnl`	Model Parallelism With Subnetwork Data Parallelism. CoRR Vaibhav Singh, Zafir Khalid, Edouard Oyallon, Eugene Belilovsky
2025	A*	`conf`	PETRA: Parallel End-to-end Training with Reversible Architectures. ICLR Stéphane Rivaud, Louis Fournier, Thomas Pumir, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
2024	J	`jnl`	ACCO: Accumulate while you Communicate, Hiding Communications in Distributed LLM Training. CoRR Adel Nabli, Louis Fournier, Pierre Erbacher, Louis Serrano, Eugene Belilovsky, Edouard Oyallon
2024	J	`jnl`	Cyclic Data Parallelism for Efficient Parallelism of Deep Neural Networks. CoRR Louis Fournier, Edouard Oyallon
2024	J	`jnl`	PETRA: Parallel End-to-end Training with Reversible Architectures. CoRR Stéphane Rivaud, Louis Fournier, Thomas Pumir, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
2024	J	`jnl`	WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average. CoRR Louis Fournier, Adel Nabli, Masih Aminbeidokhti, Marco Pedersoli, Eugene Belilovsky, Edouard Oyallon
2024	J	`jnl`	μLO: Compute-Efficient Meta-Generalization of Learned Optimizers. CoRR Benjamin Thérien, Charles-Étienne Joseph, Boris Knyazev, Edouard Oyallon, Irina Rish, Eugene Belilovsky
2023	A*	`conf`	A NeurIPS Adel Nabli, Eugene Belilovsky, Edouard Oyallon
2023	J	`jnl`	A CoRR Adel Nabli, Eugene Belilovsky, Edouard Oyallon
2023	A*	`conf`	Can Forward Gradient Match Backpropagation? ICML Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
2023	J	`jnl`	Can Forward Gradient Match Backpropagation? CoRR Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
2023	—	`book`	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	A*	`conf`	DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization. ICML Adel Nabli, Edouard Oyallon
2023	J	`jnl`	Gradient Masked Averaging for Federated Learning. Trans. Mach. Learn. Res. Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Irina Rish, Eugene Belilovsky
2023	A*	`conf`	Guiding The Last Layer in Federated Learning with Pre-Trained Models. NeurIPS Gwen Legate, Nicolas Bernier, Lucas Page-Caccia, Edouard Oyallon, Eugene Belilovsky
2023	J	`jnl`	Guiding The Last Layer in Federated Learning with Pre-Trained Models. CoRR Gwen Legate, Nicolas Bernier, Lucas Caccia, Edouard Oyallon, Eugene Belilovsky
2023	J	`jnl`	Vectorizing string entries for data processing on tables: when are larger language models better? CoRR Léo Grinsztajn, Edouard Oyallon, Myung Jun Kim, Gaël Varoquaux
2022	J	`jnl`	DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization for Time-Varying Gossips. CoRR Adel Nabli, Edouard Oyallon
2022	J	`jnl`	Gradient Masked Averaging for Federated Learning. CoRR Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Eugene Belilovsky, Irina Rish
2022	A*	`conf`	On Non-Linear operators for Geometric Deep Learning. NeurIPS Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon
2022	J	`jnl`	On Non-Linear operators for Geometric Deep Learning. CoRR Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon
2022	J	`jnl`	Why do tree-based models still outperform deep learning on tabular data? CoRR Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux
2022	A*	`conf`	Why do tree-based models still outperform deep learning on typical tabular data? NeurIPS Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux
2021	J	`jnl`	Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning. CoRR Eugene Belilovsky, Louis Leconte, Lucas Caccia, Michael Eickenberg, Edouard Oyallon
2021	—	`conf`	Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment. MLMI@MICCAI Othmane Laousy, Guillaume Chassagnon, Edouard Oyallon, Nikos Paragios, Marie-Pierre Revel, Maria Vakalopoulou
2021	J	`jnl`	Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment. CoRR Othmane Laousy, Guillaume Chassagnon, Edouard Oyallon, Nikos Paragios, Marie-Pierre Revel, Maria Vakalopoulou
2021	J	`jnl`	Interferometric Graph Transform for Community Labeling. CoRR Nathan Grinsztajn, Louis Leconte, Philippe Preux, Edouard Oyallon
2021	J	`jnl`	Low-Rank Projections of GCNs Laplacian. CoRR Nathan Grinsztajn, Philippe Preux, Edouard Oyallon
2021	A*	`conf`	The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. ICLR Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon
2021	J	`jnl`	The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. CoRR Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon
2020	A*	`conf`	Decoupled Greedy Learning of CNNs. ICML Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
2020	A*	`conf`	Interferometric Graph Transform: a Deep Unsupervised Graph Representation. ICML Edouard Oyallon
2020	J	`jnl`	Interferometric Graph Transform: a Deep Unsupervised Graph Representation. CoRR Edouard Oyallon
2020	J	`jnl`	Kymatio: Scattering Transforms in Python. J. Mach. Learn. Res. 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 Cella, Michael Eickenberg
2019	J	`jnl`	Decoupled Greedy Learning of CNNs. CoRR Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
2019	A*	`conf`	Greedy Layerwise Learning Can Scale To ImageNet. ICML Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
2019	A*	`conf`	On Lazy Training in Differentiable Programming. NeurIPS Lénaïc Chizat, Edouard Oyallon, Francis R. Bach
2019	J	`jnl`	Scattering Networks for Hybrid Representation Learning. IEEE Trans. Pattern Anal. Mach. Intell. Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky
2018	—	`conf`	Compressing the Input for CNNs with the First-Order Scattering Transform. ECCV (9) Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko
2018	J	`jnl`	Compressing the Input for CNNs with the First-Order Scattering Transform. CoRR Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko
2018	J	`jnl`	Greedy Layerwise Learning Can Scale to ImageNet. CoRR Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
2018	J	`jnl`	Kymatio: Scattering Transforms in Python. CoRR 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 Cella, Michael Eickenberg
2018	—	`conf`	Nonlinear Acceleration of CNNs. ICLR (Workshop) Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach
2018	J	`jnl`	Nonlinear Acceleration of CNNs. CoRR Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach
2018	J	`jnl`	Nonlinear Acceleration of Deep Neural Networks. CoRR Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach
2018	J	`jnl`	Scattering Networks for Hybrid Representation Learning. CoRR Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky
2018	—	`conf`	i-RevNet: Deep Invertible Networks. ICLR (Poster) Jörn-Henrik Jacobsen, Arnold W. M. Smeulders, Edouard Oyallon
2018	J	`jnl`	i-RevNet: Deep Invertible Networks. CoRR Jörn-Henrik Jacobsen, Arnold W. M. Smeulders, Edouard Oyallon
2017	—	`—`	Analyzing and Introducing Structures in Deep Convolutional Neural Networks. (Analyse et structuration des réseaux de neurones convolutifs profonds). Edouard Oyallon
2017	A*	`conf`	Building a Regular Decision Boundary with Deep Networks. CVPR Edouard Oyallon
2017	J	`jnl`	Building a Regular Decision Boundary with Deep Networks. CoRR Edouard Oyallon
2017	J	`jnl`	Multiscale Hierarchical Convolutional Networks. CoRR Jörn-Henrik Jacobsen, Edouard Oyallon, Stéphane Mallat, Arnold W. M. Smeulders
2017	A*	`conf`	Scaling the Scattering Transform: Deep Hybrid Networks. ICCV Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko
2017	J	`jnl`	Scaling the Scattering Transform: Deep Hybrid Networks. CoRR Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko
2015	J	`jnl`	An Analysis of the SURF Method. Image Process. Line Edouard Oyallon, Julien Rabin
2015	A*	`conf`	Deep roto-translation scattering for object classification. CVPR Edouard Oyallon, Stéphane Mallat
2014	J	`jnl`	Deep Roto-Translation Scattering for Object Classification. CoRR Edouard Oyallon, Stéphane Mallat
2014	—	`conf`	Generic Deep Networks with Wavelet Scattering. ICLR (Workshop Poster) Edouard Oyallon, Stéphane Mallat, Laurent Sifre