| A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation F Perazzi, J Pont-Tuset, B McWilliams, L Van Gool, M Gross, ... | 2217 | 2016 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 617 | 2024 |
| 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 | 483 | 2017 |
| Neural importance sampling T Müller, B McWilliams, F Rousselle, M Gross, J Novák ACM Transactions on Graphics (TOG) 38 (5), 145, 2019 | 359 | 2019 |
| 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 | 353 | 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 | 321 | 2018 |
| Representation learning via invariant causal mechanisms J Mitrovic, B McWilliams, J Walker, L Buesing, C Blundell arXiv preprint arXiv:2010.07922, 2020 | 266 | 2020 |
| 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 | 212 | 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 | 197 | 2018 |
| Variance reduced stochastic gradient descent with neighbors T Hofmann, A Lucchi, S Lacoste-Julien, B McWilliams Advances in Neural Information Processing Systems 28, 2015 | 182 | 2015 |
| 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 | 110 | 2017 |
| Twhin-bert: A socially-enriched pre-trained language model for multilingual tweet representations at twitter X Zhang, Y Malkov, O Florez, S Park, B McWilliams, J Han, A El-Kishky Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and …, 2023 | 98 | 2023 |
| 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 | 97 | 2020 |
| Pushing the limits of self-supervised resnets: Can we outperform supervised learning without labels on imagenet? N Tomasev, I Bica, B McWilliams, L Buesing, R Pascanu, C Blundell, ... arXiv preprint arXiv:2201.05119, 2022 | 91 | 2022 |
| Subspace clustering of high-dimensional data: a predictive approach B McWilliams, G Montana Data Mining and Knowledge Discovery 28, 736-772, 2014 | 88 | 2014 |
| 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 | 85 | 2014 |
| 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 | 80 | 2020 |
| 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 | 63 | 2014 |
| Kernel-predicting convolutional neural networks for denoising T Vogels, J Novák, F Rousselle, B McWilliams US Patent 10,475,165, 2019 | 56 | 2019 |
| Correlated random features for fast semi-supervised learning B McWilliams, D Balduzzi, JM Buhmann Advances in Neural Information Processing Systems 26, 2013 | 56 | 2013 |
Jan NovákPrincipal research scientist at NVIDIA Research確認したメール アドレス: nvidia.com