Thijs Vogels Pronunciation: /ˈtɛi̯s/

PhD student in Machine Learning & Optimization at EPFL
thijs.vogels@[epfl].ch

About

I am a PhD student at EPFL under the supervision of Martin Jaggi. I work on tools for communication-efficient distributed machine learning. Before joining EPFL, I worked as a lab associate at Disney Research. I received a B.Sc. from University College Roosevelt, an honours college from Utrecht University, and a M.Sc. from ETH Zürich in Computational Science & Engineering.

Publications

Teaser for “Beyond spectral gap: the role of topology in decentralized learning”

Beyond spectral gap: the role of topology in decentralized learning

Authors
Published
Advances in Neural Information Processing Systems 35 (NeurIPS 2022)
Teaser for “RelaySum for Decentralized Deep Learning on Heterogeneous Data”

RelaySum for Decentralized Deep Learning on Heterogeneous Data

Authors
Published
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
Teaser for “Deep Compositional Denoising for High‐quality Monte Carlo Rendering”

Deep Compositional Denoising for High‐quality Monte Carlo Rendering

Authors
Published
Computer Graphics Forum 40, 2021
Teaser for “Practical Low-Rank Communication Compression in Decentralized Deep Learning”

Practical Low-Rank Communication Compression in Decentralized Deep Learning

Authors
Published
Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Vancouver, Canada, December 7 – December 12, 2020.
Teaser for “Optimizer Benchmarking Needs to Account for Hyperparameter Tuning”

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning

Authors
Published
Proceedings of the International Conference on Machine Learning (ICML 2020)
Vienna, Austria, July 13 – July 18, 2020.
Teaser for “PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization”

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

Authors
Published
Advances in Neural Information Processing Systems 32 (NeurIPS 2019)
Vancouver, Canada, December 8 – December 14, 2019.

Denoising with Kernel Prediction and Asymmetric Loss Functions

Authors
Published
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2018), vol. 37, no. 4.
Vancouver, Canada, August 12 – August 16, 2018.
Teaser for “Kernel-predicting Convolutional Networks for Denoising Monte Carlo Renderings”

Kernel-predicting Convolutional Networks for Denoising Monte Carlo Renderings

Authors
Published
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2017), vol. 36, no. 4.
Los Angeles, USA, July 05 – August 03, 2017.
Teaser for “Kernel-predicting Convolutional Neural Networks for Denoising Monte Carlo Renderings”

Kernel-predicting Convolutional Neural Networks for Denoising Monte Carlo Renderings

Author
Published
Master’s thesis, ETH Zürich, 2016.
Awarded
Fritz Kutter Award 2017
Teaser for “Web2Text: Deep Structured Boilerplate Removal”

Web2Text: Deep Structured Boilerplate Removal

Authors
Published
European Conference on Information Retrieval, 167–179.
Grenoble, France, March 26 – March 29, 2018.
Teaser for “Towards a Burglary Risk Profiler Using Demographic and Spatial Factors”

Towards a Burglary Risk Profiler Using Demographic and Spatial Factors

Authors
Published
Web Information Systems Engineering (WISE) 16, 586–600.
Miami, USA, November 1 – November 3, 2015.