Towards a Burglary Risk Profiler Using Demographic and Spatial Factors. - Cristina Kadar; Grammatiki Zanni; Thijs Vogels; Irena Pletikosa Cvijikj (WISE (1), 2015)

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)

Web2Text: Deep Structured Boilerplate Removal. - Thijs Vogels; Octavian-Eugen Ganea; Carsten Eickhoff (ECIR, 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)

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. - Thijs Vogels; Sai Praneeth Karimireddy; Martin Jaggi (NeurIPS, 2019)

On the Tunability of Optimizers in Deep Learning. - Prabhu Teja Sivaprasad; Florian Mai; Thijs Vogels; Martin Jaggi; François Fleuret (CoRR, 2019)

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning. - Prabhu Teja Sivaprasad; Florian Mai; Thijs Vogels; Martin Jaggi; François Fleuret (ICML, 2020)

Practical Low-Rank Communication Compression in Decentralized Deep Learning. - Thijs Vogels; Sai Praneeth Karimireddy; Martin Jaggi (NeurIPS, 2020)

PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning. - Thijs Vogels; Sai Praneeth Karimireddy; Martin Jaggi (CoRR, 2020)

RelaySum for Decentralized Deep Learning on Heterogeneous Data. - Thijs Vogels; Lie He; Anastasia Koloskova; Sai Praneeth Karimireddy; Tao Lin 004; Sebastian U. Stich; Martin Jaggi (NeurIPS, 2021)

Deep Compositional Denoising for High-quality Monte Carlo Rendering. - Xianyao Zhang; Marco Manzi; Thijs Vogels; Henrik Dahlberg; Markus Gross 001; Marios Papas (Comput. Graph. Forum, 2021)

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)

MultiMoDN - Multimodal, Multi-Task, Interpretable Modular Networks. - Vinitra Swamy; Malika Satayeva; Jibril Frej; Thierry Bossy; Thijs Vogels; Martin Jaggi; Tanja Käser; Mary-Anne Hartley (NeurIPS, 2023)

Beyond spectral gap (extended): The role of the topology in decentralized learning. - Thijs Vogels; Hadrien Hendrikx; Martin Jaggi (CoRR, 2023)

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning. - Thijs Vogels; Hadrien Hendrikx; Martin Jaggi (J. Mach. Learn. Res., 2023)

LASER: Linear Compression in Wireless Distributed Optimization. - Ashok Vardhan Makkuva; Marco Bondaschi; Thijs Vogels; Martin Jaggi; Hyeji Kim; Michael Gastpar (ICML, 2024)

Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits. - Daniel Morales-Brotons; Thijs Vogels; Hadrien Hendrikx (Trans. Mach. Learn. Res., 2024)

Accurate and scalable exchange-correlation with deep learning. - Giulia Luise; Chin-Wei Huang; Thijs Vogels; Derk P. Kooi; Sebastian Ehlert; Stephanie Lanius; Klaas J. H. Giesbertz; Amir Karton; Deniz Gunceler; Megan Stanley; Wessel P. Bruinsma; Lin Huang; et al. (CoRR, 2025)