| 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 | 265 | 2017 |
| PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization T Vogels, SP Karimireddy, M Jaggi NeurIPS 2019, 14259-14268, 2019 | 199 | 2019 |
| 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 | 144 | 2018 |
| 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 | 51 | 2020 |
| Kernel-predicting convolutional neural networks for denoising T Vogels, J Novák, F Rousselle, B McWilliams US Patent 10,475,165, 2019 | 44 | 2019 |
| Web2text: Deep structured boilerplate removal T Vogels, OE Ganea, C Eickhoff Advances in Information Retrieval: 40th European Conference on IR Research …, 2018 | 40 | 2018 |
| 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 | 36* | 2020 |
| Practical low-rank communication compression in decentralized deep learning T Vogels, SP Karimireddy, M Jaggi Advances in Neural Information Processing Systems 33, 14171-14181, 2020 | 35* | 2020 |
| Relaysum for decentralized deep learning on heterogeneous data T Vogels, L He, A Koloskova, SP Karimireddy, T Lin, SU Stich, M Jaggi Advances in Neural Information Processing Systems 34, 28004-28015, 2021 | 33 | 2021 |
| Denoising monte carlo renderings using progressive neural networks T Vogels, F Rousselle, B McWilliams, M Meyer, J Novak US Patent 10,607,319, 2020 | 32 | 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 | 14 | 2020 |
| 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 | 13 | 2020 |
| 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 | 7 | 2015 |
| Deep Compositional Denoising for High‐quality Monte Carlo Rendering X Zhang, M Manzi, T Vogels, H Dahlberg, M Gross, M Papas Computer Graphics Forum 40 (4), 1-13, 2021 | 5 | 2021 |
| 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 | 5 | 2020 |
| Beyond spectral gap: The role of the topology in decentralized learning T Vogels, H Hendrikx, M Jaggi arXiv preprint arXiv:2206.03093, 2022 | 4 | 2022 |
| 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 | 3 | 2020 |
| Modular Clinical Decision Support Networks (MoDN)—Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments C Trottet, T Vogels, K Keitel, A Kulunkina, R Tan, L Cobuccio, M Jaggi, ... medRxiv, 2022.08. 17.22278908, 2022 | 2 | 2022 |
| 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 | 2 | 2020 |
| Beyond spectral gap (extended): The role of the topology in decentralized learning T Vogels, H Hendrikx, M Jaggi arXiv preprint arXiv:2301.02151, 2023 | 1 | 2023 |
Brian McWilliamsResearch Scientist, GoogleVerified email at google.com
