| 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 | 155 | 2017 |
| 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 | 68 | 2018 |
| PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization T Vogels, SP Karimireddy, M Jaggi NeurIPS 2019, 14259-14268, 2019 | 60 | 2019 |
| 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 | 20 | 2020 |
| Kernel-predicting convolutional neural networks for denoising T Vogels, J Novák, F Rousselle, B Mcwilliams US Patent 10,475,165, 2019 | 20 | 2019 |
| Web2text: Deep structured boilerplate removal T Vogels, OE Ganea, C Eickhoff European Conference on Information Retrieval, 167-179, 2018 | 19 | 2018 |
| Denoising monte carlo renderings using progressive neural networks T Vogels, F Rousselle, B Mcwilliams, M Meyer, J Novak US Patent 10,607,319, 2020 | 9 | 2020 |
| 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 | 7 | 2020 |
| 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 | 4 | 2020 |
| 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 | 4 | 2019 |
| 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 | 4 | 2015 |
| 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 | 3 | 2020 |
| 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 | 2 | 2019 |
| 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 | 1 | 2020 |
| 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 |
Brian McWilliamsResearch Scientist, DeepMindBestätigte E-Mail-Adresse bei google.com
