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Edouard Oyallon
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2020 – today
- 2024
- [i31]Louis Fournier, Edouard Oyallon:
Cyclic Data Parallelism for Efficient Parallelism of Deep Neural Networks. CoRR abs/2403.08837 (2024) - [i30]Louis Fournier, Adel Nabli, Masih Aminbeidokhti, Marco Pedersoli, Eugene Belilovsky, Edouard Oyallon:
WASH: Train your Ensemble with Communication-Efficient Weight Shuffling, then Average. CoRR abs/2405.17517 (2024) - [i29]Benjamin Thérien, Charles-Étienne Joseph, Boris Knyazev, Edouard Oyallon, Irina Rish, Eugene Belilovsky:
μLO: Compute-Efficient Meta-Generalization of Learned Optimizers. CoRR abs/2406.00153 (2024) - [i28]Stéphane Rivaud, Louis Fournier, Thomas Pumir, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
PETRA: Parallel End-to-end Training with Reversible Architectures. CoRR abs/2406.02052 (2024) - [i27]Adel Nabli, Louis Fournier, Pierre Erbacher, Louis Serrano, Eugene Belilovsky, Edouard Oyallon:
ACCO: Accumulate while you Communicate, Hiding Communications in Distributed LLM Training. CoRR abs/2406.02613 (2024) - 2023
- [b2]Edouard Oyallon:
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). Sorbonne University, Paris, France, 2023 - [j4]Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Irina Rish, Eugene Belilovsky:
Gradient Masked Averaging for Federated Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c19]Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Can Forward Gradient Match Backpropagation? ICML 2023: 10249-10264 - [c18]Adel Nabli, Edouard Oyallon:
DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization. ICML 2023: 25604-25626 - [c17]Gwen Legate, Nicolas Bernier, Lucas Page-Caccia, Edouard Oyallon, Eugene Belilovsky:
Guiding The Last Layer in Federated Learning with Pre-Trained Models. NeurIPS 2023 - [c16]Adel Nabli, Eugene Belilovsky, Edouard Oyallon:
A2CiD2: Accelerating Asynchronous Communication in Decentralized Deep Learning. NeurIPS 2023 - [i26]Gwen Legate, Nicolas Bernier, Lucas Caccia, Edouard Oyallon, Eugene Belilovsky:
Guiding The Last Layer in Federated Learning with Pre-Trained Models. CoRR abs/2306.03937 (2023) - [i25]Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Can Forward Gradient Match Backpropagation? CoRR abs/2306.06968 (2023) - [i24]Adel Nabli, Eugene Belilovsky, Edouard Oyallon:
A2CiD2: Accelerating Asynchronous Communication in Decentralized Deep Learning. CoRR abs/2306.08289 (2023) - [i23]Léo Grinsztajn, Edouard Oyallon, Myung Jun Kim, Gaël Varoquaux:
Vectorizing string entries for data processing on tables: when are larger language models better? CoRR abs/2312.09634 (2023) - 2022
- [c15]Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux:
Why do tree-based models still outperform deep learning on typical tabular data? NeurIPS 2022 - [c14]Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon:
On Non-Linear operators for Geometric Deep Learning. NeurIPS 2022 - [i22]Irene Tenison, Sai Aravind Sreeramadas, Vaikkunth Mugunthan, Edouard Oyallon, Eugene Belilovsky, Irina Rish:
Gradient Masked Averaging for Federated Learning. CoRR abs/2201.11986 (2022) - [i21]Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon:
On Non-Linear operators for Geometric Deep Learning. CoRR abs/2207.03485 (2022) - [i20]Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux:
Why do tree-based models still outperform deep learning on tabular data? CoRR abs/2207.08815 (2022) - [i19]Adel Nabli, Edouard Oyallon:
DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization for Time-Varying Gossips. CoRR abs/2208.00779 (2022) - 2021
- [c13]Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon:
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. ICLR 2021 - [c12]Othmane Laousy, Guillaume Chassagnon, Edouard Oyallon, Nikos Paragios, Marie-Pierre Revel, Maria Vakalopoulou:
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment. MLMI@MICCAI 2021: 317-326 - [i18]Louis Thiry, Michael Arbel, Eugene Belilovsky, Edouard Oyallon:
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods. CoRR abs/2101.07528 (2021) - [i17]Nathan Grinsztajn, Louis Leconte, Philippe Preux, Edouard Oyallon:
Interferometric Graph Transform for Community Labeling. CoRR abs/2106.05875 (2021) - [i16]Eugene Belilovsky, Louis Leconte, Lucas Caccia, Michael Eickenberg, Edouard Oyallon:
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning. CoRR abs/2106.06401 (2021) - [i15]Nathan Grinsztajn, Philippe Preux, Edouard Oyallon:
Low-Rank Projections of GCNs Laplacian. CoRR abs/2106.07360 (2021) - [i14]Othmane Laousy, Guillaume Chassagnon, Edouard Oyallon, Nikos Paragios, Marie-Pierre Revel, Maria Vakalopoulou:
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment. CoRR abs/2107.12800 (2021) - 2020
- [j3]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:
Kymatio: Scattering Transforms in Python. J. Mach. Learn. Res. 21: 60:1-60:6 (2020) - [c11]Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Decoupled Greedy Learning of CNNs. ICML 2020: 736-745 - [c10]Edouard Oyallon:
Interferometric Graph Transform: a Deep Unsupervised Graph Representation. ICML 2020: 7434-7444 - [i13]Edouard Oyallon:
Interferometric Graph Transform: a Deep Unsupervised Graph Representation. CoRR abs/2006.05722 (2020)
2010 – 2019
- 2019
- [j2]Edouard Oyallon
, Sergey Zagoruyko, Gabriel Huang
Scattering Networks for Hybrid Representation Learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(9): 2208-2221 (2019) - [c9]Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Greedy Layerwise Learning Can Scale To ImageNet. ICML 2019: 583-593 - [c8]Lénaïc Chizat, Edouard Oyallon, Francis R. Bach:
On Lazy Training in Differentiable Programming. NeurIPS 2019: 2933-2943 - [i12]Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Decoupled Greedy Learning of CNNs. CoRR abs/1901.08164 (2019) - 2018
- [c7]Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko:
Compressing the Input for CNNs with the First-Order Scattering Transform. ECCV (9) 2018: 305-320 - [c6]Jörn-Henrik Jacobsen, Arnold W. M. Smeulders, Edouard Oyallon:
i-RevNet: Deep Invertible Networks. ICLR (Poster) 2018 - [c5]Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach:
Nonlinear Acceleration of CNNs. ICLR (Workshop) 2018 - [i11]Jörn-Henrik Jacobsen, Arnold W. M. Smeulders, Edouard Oyallon:
i-RevNet: Deep Invertible Networks. CoRR abs/1802.07088 (2018) - [i10]Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach:
Nonlinear Acceleration of Deep Neural Networks. CoRR abs/1805.09639 (2018) - [i9]Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis R. Bach:
Nonlinear Acceleration of CNNs. CoRR abs/1806.00370 (2018) - [i8]Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko, Eugene Belilovsky:
Scattering Networks for Hybrid Representation Learning. CoRR abs/1809.06367 (2018) - [i7]Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko:
Compressing the Input for CNNs with the First-Order Scattering Transform. CoRR abs/1809.10200 (2018) - [i6]Mathieu Andreux
Kymatio: Scattering Transforms in Python. CoRR abs/1812.11214 (2018) - [i5]Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Greedy Layerwise Learning Can Scale to ImageNet. CoRR abs/1812.11446 (2018) - 2017
- [b1]Edouard Oyallon:
Analyzing and Introducing Structures in Deep Convolutional Neural Networks. (Analyse et structuration des réseaux de neurones convolutifs profonds). PSL Research University, Paris, France, 2017 - [c4]Edouard Oyallon:
Building a Regular Decision Boundary with Deep Networks. CVPR 2017: 1886-1894 - [c3]Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko:
Scaling the Scattering Transform: Deep Hybrid Networks. ICCV 2017: 5619-5628 - [i4]Edouard Oyallon:
Building a Regular Decision Boundary with Deep Networks. CoRR abs/1703.01775 (2017) - [i3]Jörn-Henrik Jacobsen, Edouard Oyallon, Stéphane Mallat, Arnold W. M. Smeulders:
Multiscale Hierarchical Convolutional Networks. CoRR abs/1703.04140 (2017) - [i2]Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko:
Scaling the Scattering Transform: Deep Hybrid Networks. CoRR abs/1703.08961 (2017) - 2015
- [j1]Edouard Oyallon, Julien Rabin:
An Analysis of the SURF Method. Image Process. Line 5: 176-218 (2015) - [c2]Edouard Oyallon, Stéphane Mallat:
Deep roto-translation scattering for object classification. CVPR 2015: 2865-2873 - 2014
- [c1]Edouard Oyallon, Stéphane Mallat, Laurent Sifre:
Generic Deep Networks with Wavelet Scattering. ICLR (Workshop Poster) 2014 - [i1]Edouard Oyallon, Stéphane Mallat:
Deep Roto-Translation Scattering for Object Classification. CoRR abs/1412.8659 (2014)
Coauthor Index
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last updated on 2024-08-02 20:17 CEST by the dblp team
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