| PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization T Vogels, SP Karimireddy, M Jaggi NeurIPS 2019, 14259-14268, 2019 | 448 | 2019 |
| 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 | 406 | 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 | 229 | 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 | 106 | 2020 |
| Exponential moving average of weights in deep learning: Dynamics and benefits DM Brotons, T Vogels, H Hendrikx Transactions on Machine Learning Research Journal, 2024 | 101* | 2024 |
| 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 | 84 | 2021 |
| Kernel-predicting convolutional neural networks for denoising T Vogels, J Novák, F Rousselle, B McWilliams US Patent 10,475,165, 2019 | 80 | 2019 |
| 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 | 79* | 2020 |
| Web2text: Deep structured boilerplate removal T Vogels, OE Ganea, C Eickhoff European Conference on Information Retrieval, 167-179, 2018 | 65 | 2018 |
| 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 | 64* | 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 | 57 | 2020 |
| Beyond spectral gap: The role of the topology in decentralized learning T Vogels, H Hendrikx, M Jaggi Advances in Neural Information Processing Systems 35, 15039-15050, 2022 | 50 | 2022 |
| 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 | 38 | 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 | 37 | 2020 |
| Multimodn—multimodal, multi-task, interpretable modular networks V Swamy, M Satayeva, J Frej, T Bossy, T Vogels, M Jaggi, T Käser, ... Advances in neural information processing systems 36, 28115-28138, 2023 | 33 | 2023 |
| Temporal techniques of denoising Monte Carlo renderings using neural networks T Vogels, F Rousselle, J Novak, B McWilliams, M Meyer, A Harvill US Patent 11,532,073, 2022 | 22 | 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 | 19 | 2020 |
| 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 | 18 | 2020 |
| 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 | 17 | 2021 |
| Modular clinical decision support networks (MoDN)—updatable, interpretable, and portable predictions for evolving clinical environments C Trottet, T Vogels, K Keitel, AV Kulinkina, R Tan, L Cobuccio, M Jaggi, ... PLOS digital health 2 (7), e0000108, 2023 | 12 | 2023 |
Brian McWilliamsGoogle DeepMindVerified email at google.com
