Thijs Vogels
Thijs Vogels
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Title
Cited by
Cited by
Year
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
1242017
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
482018
PowerSGD: Practical low-rank gradient compression for distributed optimization
T Vogels, SP Karimireddy, M Jaggi
Advances in Neural Information Processing Systems, 14259-14268, 2019
192019
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
172020
Web2text: Deep structured boilerplate removal
T Vogels, OE Ganea, C Eickhoff
European Conference on Information Retrieval, 167-179, 2018
142018
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
Optimizer benchmarking needs to account for hyperparameter tuning
PT Sivaprasad, F Mai, T Vogels, M Jaggi, F Fleuret
Proceedings of the 37th International Conference on Machine Learning, 2020
32020
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
22020
Kernel-predicting convolutional neural networks for denoising
T Vogels, J Novák, F Rousselle, B McWilliams
US Patent 10,475,165, 2019
22019
On the Tunability of Optimizers in Deep Learning
PT Sivaprasad, F Mai, T Vogels, M Jaggi, F Fleuret
arXiv preprint arXiv:1910.11758, 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 generative adversarial neural networks
T Vogels, F Rousselle, B McWilliams, M Meyer, J Novak
US Patent 10,586,310, 2020
12020
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
12019
PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning
T Vogels, SP Karimireddy, M Jaggi
arXiv preprint arXiv:2008.01425, 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
Denoising monte carlo renderings using progressive neural networks
T Vogels, F Rousselle, B McWilliams, M Meyer, J Novak
US Patent App. 16/789,025, 2020
2020
Denoising monte carlo renderings using machine learning with importance sampling
T Vogels, F Rousselle, B Mcwilliams, M Meyer, J Novak
US Patent App. 16/735,079, 2020
2020
Denoising monte carlo renderings using progressive neural networks
T Vogels, F Rousselle, B McWilliams, M Meyer, J Novak
US Patent 10,607,319, 2020
2020
On the Tunability of Optimizers in Deep Learning
F Mai, T Vogels, M Jaggi, F Fleuret
2019
KERNEL-PREDICTING CONVOLUTIONAL NEURAL NETWORKS FOR DENOISING MONTE CARLO RENDERINGS
A Krause, B McWilliams, J Novák
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Articles 1–20