| A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation F Perazzi, J Pont-Tuset, B McWilliams, L Van Gool, M Gross, ... | 810 | 2016 |
| The Shattered Gradients Problem: If resnets are the answer, then what is the question? D Balduzzi, M Frean, L Leary, JP Lewis, KWD Ma, B McWilliams arXiv preprint arXiv:1702.08591, 2017 | 173 | 2017 |
| Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings S Bako, T Vogels, B McWilliams, M Meyer, J Novak, A Harvill, P Sen, ... ACM Transactions on Graphics (Proceedings of SIGGRAPH 2017) 36 (4), 2017 | 143 | 2017 |
| A Fully Progressive Approach to Single-Image Super-Resolution Y Wang, F Perazzi, B McWilliams, A Sorkine-Hornung, ... arXiv preprint arXiv:1804.02900, 2018 | 119 | 2018 |
| Variance reduced stochastic gradient descent with neighbors T Hofmann, A Lucchi, S Lacoste-Julien, B McWilliams arXiv preprint arXiv:1506.03662, 2015 | 102 | 2015 |
| Neural importance sampling T Müller, B McWilliams, F Rousselle, M Gross, J Novák ACM Transactions on Graphics (TOG) 38 (5), 145, 2019 | 85 | 2019 |
| PhaseNet for Video Frame Interpolation S Meyer, A Djelouah, B McWilliams, A Sorkine-Hornung, M Gross, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 71 | 2018 |
| Subspace clustering of high-dimensional data: a predictive approach B McWilliams, G Montana Data Mining and Knowledge Discovery 28 (3), 736-772, 2014 | 62 | 2014 |
| Denoising with Kernel Prediction and Asymmetric Loss Functions T Vogels, F Rousselle, B McWilliams, G Röthlin, A Harvill, D Adler, ... ACM Transactions on Graphics (Proceedings of SIGGRAPH 2018), 2018 | 59 | 2018 |
| Fast and robust least squares estimation in corrupted linear models B McWilliams, G Krummenacher, M Lucic, JM Buhmann arXiv preprint arXiv:1406.3175, 2014 | 45 | 2014 |
| Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks S Kallweit, T Muller, B McWilliams, M Gross, J Novak arXiv preprint arXiv:1709.05418, 2017 | 43 | 2017 |
| Correlated random features for fast semi-supervised learning B McWilliams, D Balduzzi, JM Buhmann arXiv preprint arXiv:1306.5554, 2013 | 42 | 2013 |
| Dual-loco: Distributing statistical estimation using random projections C Heinze, B McWilliams, N Meinshausen Artificial Intelligence and Statistics, 875-883, 2016 | 38 | 2016 |
| Learning Outlier Ensembles: The Best of Both Worlds—Supervised and Unsupervised B Micenková, B McWilliams, I Assent Proceedings of the ACM SIGKDD 2014 Workshop on Outlier Detection and …, 2014 | 37 | 2014 |
| A variance reduced stochastic newton method A Lucchi, B McWilliams, T Hofmann arXiv preprint arXiv:1503.08316, 2015 | 35 | 2015 |
| LOCO: Distributing ridge regression with random projections C Heinze, B McWilliams, N Meinshausen, G Krummenacher arXiv:1406.3469, 2014 | 30* | 2014 |
| Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks D Balduzzi, B McWilliams, T Butler-Yeoman arxiv.org, 2016 | 25 | 2016 |
| 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 |
| Scalable Adaptive Stochastic Optimization Using Random Projections G Krummenacher, B McWilliams, Y Kilcher, J Buhmann, N Meinshausen Advances in Neural Information Processing Systems 29, 2016 | 18* | 2016 |
| Kernel-predicting convolutional neural networks for denoising T Vogels, J Novák, F Rousselle, B McWilliams US Patent 10,475,165, 2019 | 16 | 2019 |
Jan NovákSenior research scientist at NVIDIA ResearchVerified email at nvidia.com
