Thijs Vogels
Thijs Vogels
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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
1552017
Denoising with kernel prediction and asymmetric loss functions
T Vogels, F Rousselle, B McWilliams, G Röthlin, A Harvill, D Adler, ...
ACM Transactions on Graphics (TOG) 37 (4), 1-15, 2018
682018
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
T Vogels, SP Karimireddy, M Jaggi
NeurIPS 2019, 14259-14268, 2019
602019
Denoising Monte Carlo renderings using machine learning with importance sampling
T Vogels, F Rousselle, B Mcwilliams, M Meyer, J Novak
US Patent 10,572,979, 2020
202020
Kernel-predicting convolutional neural networks for denoising
T Vogels, J Novák, F Rousselle, B Mcwilliams
US Patent 10,475,165, 2019
202019
Web2text: Deep structured boilerplate removal
T Vogels, OE Ganea, C Eickhoff
European Conference on Information Retrieval, 167-179, 2018
192018
Denoising monte carlo renderings using progressive neural networks
T Vogels, F Rousselle, B Mcwilliams, M Meyer, J Novak
US Patent 10,607,319, 2020
92020
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
72020
Denoising Monte Carlo renderings using generative adversarial neural networks
T Vogels, F Rousselle, B Mcwilliams, M Meyer, J Novak
US Patent 10,586,310, 2020
42020
Practical Low-Rank Communication Compression in Decentralized Deep Learning
T Vogels, SP Karimireddy, M Jaggi
Advances in Neural Information Processing Systems 33, 2020
4*2020
On the tunability of optimizers in deep learning
PT Sivaprasad, F Mai, T Vogels, M Jaggi, F Fleuret
Idiap, 2019
42019
Towards a Burglary Risk Profiler Using Demographic and Spatial Factors
C Kadar, G Zanni, T Vogels, I Pletikosa
Web Information Systems Engineering (WISE) 16, 586-600, 2015
42015
Denoising Monte Carlo renderings using neural networks with asymmetric loss
T Vogels, F Rousselle, J Novak, B Mcwilliams, M Meyer, A Harvill
US Patent 10,699,382, 2020
32020
Temporal techniques of denoising monte carlo renderings using neural networks
T Vogels, F Rousselle, J Novak, B McWilliams, M Meyer, A Harvill, D Adler
US Patent App. 16/050,314, 2019
22019
Multi-scale architecture of denoising monte carlo renderings using neural networks
T Vogels, F Rousselle, J Novak, B Mcwilliams, M Meyer, A Harvill
US Patent 10,672,109, 2020
12020
Denoising Monte Carlo renderings using machine learning with importance sampling
T Vogels, F Rousselle, B McWilliams, M Meyer, J Novak
US Patent 10,789,686, 2020
2020
Adaptive sampling in Monte Carlo renderings using error-predicting neural networks
T Vogels, F Rousselle, J Novak, B Mcwilliams, M Meyer, A Harvill
US Patent 10,706,508, 2020
2020
On the Tunability of Optimizers in Deep Learning
F Mai, T Vogels, M Jaggi, F Fleuret
2019
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