Thijs Vogels

According to our database, Thijs Vogels authored at least 16 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

LASER: Linear Compression in Wireless Distributed Optimization.

Ashok Vardhan Makkuva, Marco Bondaschi, Thijs Vogels, Martin Jaggi, Hyeji Kim, Michael C. Gastpar

CoRR, 2023

Beyond spectral gap (extended): The role of the topology in decentralized learning.

Thijs Vogels, Hadrien Hendrikx, Martin Jaggi

CoRR, 2023

MultiMoDN - Multimodal, Multi-Task, Interpretable Modular Networks.

Vinitra Swamy, Malika Satayeva, Jibril Frej, Thierry Bossy, Thijs Vogels, Martin Jaggi, Tanja Käser, Mary-Anne Hartley

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Modular Clinical Decision Support Networks (MoDN) - Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments.

Cécile Trottet, Thijs Vogels, Martin Jaggi, Mary-Anne Hartley

CoRR, 2022

Beyond spectral gap: the role of the topology in decentralized learning.

Thijs Vogels, Hadrien Hendrikx, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Deep Compositional Denoising for High-quality Monte Carlo Rendering.

Xianyao Zhang, Marco Manzi, Thijs Vogels, Henrik Dahlberg, Markus H. Gross, Marios Papas

Comput. Graph. Forum, 2021

RelaySum for Decentralized Deep Learning on Heterogeneous Data.

Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning.

Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

CoRR, 2020

Practical Low-Rank Communication Compression in Decentralized Deep Learning.

Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning.

Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

On the Tunability of Optimizers in Deep Learning.

Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret

CoRR, 2019

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization.

Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Denoising with kernel prediction and asymmetric loss functions.

Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Gerhard Röthlin, Alex Harvill, David Adler, Mark Meyer, Jan Novák

ACM Trans. Graph., 2018

Web2Text: Deep Structured Boilerplate Removal.

Thijs Vogels, Octavian-Eugen Ganea, Carsten Eickhoff

Proceedings of the Advances in Information Retrieval, 2018

2017

Kernel-predicting convolutional networks for denoising Monte Carlo renderings.

Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony DeRose, Fabrice Rousselle

ACM Trans. Graph., 2017

2015

Towards a Burglary Risk Profiler Using Demographic and Spatial Factors.

Cristina Kadar, Grammatiki Zanni, Thijs Vogels, Irena Pletikosa Cvijikj

Proceedings of the Web Information Systems Engineering - WISE 2015, 2015