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Thijs Vogels
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2020 – today
- 2024
- [j5]Daniel Morales-Brotons, Thijs Vogels, Hadrien Hendrikx:
Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits. Trans. Mach. Learn. Res. 2024 (2024) - [c9]Ashok Vardhan Makkuva, Marco Bondaschi, Thijs Vogels, Martin Jaggi, Hyeji Kim, Michael Gastpar:
LASER: Linear Compression in Wireless Distributed Optimization. ICML 2024 - 2023
- [j4]Thijs Vogels, Hadrien Hendrikx, Martin Jaggi:
Beyond Spectral Gap: The Role of the Topology in Decentralized Learning. J. Mach. Learn. Res. 24: 355:1-355:31 (2023) - [c8]Vinitra Swamy, Malika Satayeva, Jibril Frej, Thierry Bossy, Thijs Vogels, Martin Jaggi, Tanja Käser, Mary-Anne Hartley:
MultiMoDN - Multimodal, Multi-Task, Interpretable Modular Networks. NeurIPS 2023 - [i10]Thijs Vogels, Hadrien Hendrikx, Martin Jaggi:
Beyond spectral gap (extended): The role of the topology in decentralized learning. CoRR abs/2301.02151 (2023) - [i9]Vinitra Swamy, Malika Satayeva, Jibril Frej, Thierry Bossy, Thijs Vogels, Martin Jaggi, Tanja Käser, Mary-Anne Hartley:
MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks. CoRR abs/2309.14118 (2023) - [i8]Ashok Vardhan Makkuva, Marco Bondaschi, Thijs Vogels, Martin Jaggi, Hyeji Kim, Michael C. Gastpar:
LASER: Linear Compression in Wireless Distributed Optimization. CoRR abs/2310.13033 (2023) - 2022
- [c7]Thijs Vogels, Hadrien Hendrikx, Martin Jaggi:
Beyond spectral gap: the role of the topology in decentralized learning. NeurIPS 2022 - [i7]Thijs Vogels, Hadrien Hendrikx, Martin Jaggi:
Beyond spectral gap: The role of the topology in decentralized learning. CoRR abs/2206.03093 (2022) - [i6]Cécile Trottet, Thijs Vogels, Martin Jaggi, Mary-Anne Hartley:
Modular Clinical Decision Support Networks (MoDN) - Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments. CoRR abs/2211.06637 (2022) - 2021
- [j3]Xianyao Zhang, Marco Manzi, Thijs Vogels, Henrik Dahlberg, Markus Gross, Marios Papas:
Deep Compositional Denoising for High-quality Monte Carlo Rendering. Comput. Graph. Forum 40(4): 1-13 (2021) - [c6]Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. NeurIPS 2021: 28004-28015 - [i5]Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2110.04175 (2021) - 2020
- [c5]Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret:
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning. ICML 2020: 9036-9045 - [c4]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
Practical Low-Rank Communication Compression in Decentralized Deep Learning. NeurIPS 2020 - [i4]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning. CoRR abs/2008.01425 (2020)
2010 – 2019
- 2019
- [c3]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. NeurIPS 2019: 14236-14245 - [i3]Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi:
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. CoRR abs/1905.13727 (2019) - [i2]Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret:
On the Tunability of Optimizers in Deep Learning. CoRR abs/1910.11758 (2019) - 2018
- [j2]Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Gerhard Röthlin, Alex Harvill, David Adler, Mark Meyer, Jan Novák:
Denoising with kernel prediction and asymmetric loss functions. ACM Trans. Graph. 37(4): 124 (2018) - [c2]Thijs Vogels, Octavian-Eugen Ganea, Carsten Eickhoff:
Web2Text: Deep Structured Boilerplate Removal. ECIR 2018: 167-179 - [i1]Thijs Vogels, Octavian-Eugen Ganea, Carsten Eickhoff:
Web2Text: Deep Structured Boilerplate Removal. CoRR abs/1801.02607 (2018) - 2017
- [j1]Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony DeRose, Fabrice Rousselle:
Kernel-predicting convolutional networks for denoising Monte Carlo renderings. ACM Trans. Graph. 36(4): 97:1-97:14 (2017) - 2015
- [c1]Cristina Kadar, Grammatiki Zanni, Thijs Vogels, Irena Pletikosa Cvijikj:
Towards a Burglary Risk Profiler Using Demographic and Spatial Factors. WISE (1) 2015: 586-600
Coauthor Index
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last updated on 2024-09-14 02:03 CEST by the dblp team
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