| A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation F Perazzi, J Pont-Tuset, B McWilliams, L Van Gool, M Gross, ... | 1172 | 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 | 247 | 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 | 215 | 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 | 184 | 2018 |
| Neural importance sampling T Müller, B McWilliams, F Rousselle, M Gross, J Novák ACM Transactions on Graphics (TOG) 38 (5), 145, 2019 | 148 | 2019 |
| Variance reduced stochastic gradient descent with neighbors T Hofmann, A Lucchi, S Lacoste-Julien, B McWilliams Advances in Neural Information Processing Systems 28, 2015 | 126 | 2015 |
| 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 | 119 | 2018 |
| 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 | 108 | 2018 |
| Subspace clustering of high-dimensional data: a predictive approach B McWilliams, G Montana Data Mining and Knowledge Discovery 28 (3), 736-772, 2014 | 71 | 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 | 60 | 2017 |
| Representation learning via invariant causal mechanisms J Mitrovic, B McWilliams, J Walker, L Buesing, C Blundell arXiv preprint arXiv:2010.07922, 2020 | 56 | 2020 |
| 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 | 48 | 2014 |
| Fast and robust least squares estimation in corrupted linear models B McWilliams, G Krummenacher, M Lucic, JM Buhmann Advances in Neural Information Processing Systems 27, 2014 | 48 | 2014 |
| Correlated random features for fast semi-supervised learning B McWilliams, D Balduzzi, JM Buhmann Advances in Neural Information Processing Systems 26, 2013 | 47 | 2013 |
| Dual-loco: Distributing statistical estimation using random projections C Heinze, B McWilliams, N Meinshausen Artificial Intelligence and Statistics, 875-883, 2016 | 38 | 2016 |
| A variance reduced stochastic Newton method A Lucchi, B McWilliams, T Hofmann arXiv preprint arXiv:1503.08316, 2015 | 37 | 2015 |
| Social diversity and social preferences in mixed-motive reinforcement learning KR McKee, I Gemp, B McWilliams, EA Duéñez-Guzmán, E Hughes, ... arXiv preprint arXiv:2002.02325, 2020 | 31 | 2020 |
| 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 | 30 | 2020 |
| Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks D Balduzzi, B McWilliams, T Butler-Yeoman arxiv.org, 2016 | 30 | 2016 |
| LOCO: Distributing ridge regression with random projections C Heinze, B McWilliams, N Meinshausen, G Krummenacher arXiv:1406.3469, 2014 | 30* | 2014 |
Jan NovákSenior research scientist at NVIDIA ResearchVerified email at nvidia.com
