Articles avec mandats d'accès public - Thijs VogelsEn savoir plus
Disponibles quelque part : 8
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
T Vogels, SP Karimireddy, M Jaggi
NeurIPS 2019, 14259-14268, 2019
Exigences : Fonds national suisse
Kernel-predicting convolutional networks for denoising Monte Carlo renderings.
S Bako, T Vogels, B McWilliams, M Meyer, J Novák, A Harvill, P Sen, ...
ACM Trans. Graph. 36 (4), 97:1-97:14, 2017
Exigences : US National Science Foundation
Relaysum for decentralized deep learning on heterogeneous data
T Vogels, L He, A Koloskova, SP Karimireddy, T Lin, SU Stich, M Jaggi
Advances in Neural Information Processing Systems 34, 28004-28015, 2021
Exigences : Fonds national suisse, European Commission
Optimizer benchmarking needs to account for hyperparameter tuning
PT Sivaprasad, F Mai, T Vogels, M Jaggi, F Fleuret
International conference on machine learning, 9036-9045, 2020
Exigences : Fonds national suisse, US Department of Defense
Practical low-rank communication compression in decentralized deep learning
T Vogels, SP Karimireddy, M Jaggi
Advances in Neural Information Processing Systems 33, 14171-14181, 2020
Exigences : Fonds national suisse
Beyond spectral gap: The role of the topology in decentralized learning
T Vogels, H Hendrikx, M Jaggi
Advances in Neural Information Processing Systems 35, 15039-15050, 2022
Exigences : Fonds national suisse
Beyond spectral gap: the role of the topology in decentralized learning
T Vogels, H Hendrikx, M Jaggi
Journal of Machine Learning Research 24 (355), 1-31, 2023
Exigences : Fonds national suisse
Communication-efficient distributed training of machine learning models
T Vogels
EPFL, 2023
Exigences : Fonds national suisse
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