| Visualizing data using t-SNE L van der Maaten, G Hinton The Journal of Machine Learning Research 9 (2579-2605), 85, 2008 | 50147 | 2008 |
| Densely Connected Convolutional Networks G Huang, Z Liu, L van der Maaten, KQ Weinberger IEEE Conference on Computer Vision and Pattern Recognition, 2016 | 49774 | 2016 |
| Dimensionality reduction: A comparative review LJP Van der Maaten, EO Postma, HJ Van den Herik Technical Report TiCC TR 2009-005, 2009 | 4030* | 2009 |
| Accelerating t-SNE using Tree-Based Algorithms L Van Der Maaten The Journal of Machine Learning Research 15 (1), 3221-3245, 2014 | 3148 | 2014 |
| Clevr: A diagnostic dataset for compositional language and elementary visual reasoning J Johnson, B Hariharan, L Van Der Maaten, L Fei-Fei, C Lawrence Zitnick, ... Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 2585 | 2017 |
| 3d semantic segmentation with submanifold sparse convolutional networks B Graham, M Engelcke, L Van Der Maaten Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 1744 | 2018 |
| Self-supervised learning of pretext-invariant representations I Misra, L Maaten Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 1712 | 2020 |
| Countering adversarial images using input transformations C Guo, M Rana, M Cisse, L Van Der Maaten arXiv preprint arXiv:1711.00117, 2017 | 1693 | 2017 |
| Exploring the limits of weakly supervised pretraining D Mahajan, R Girshick, V Ramanathan, K He, M Paluri, Y Li, A Bharambe, ... Proceedings of the European conference on computer vision (ECCV), 181-196, 2018 | 1665 | 2018 |
| The Llama 3 Herd of Models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2024 | 1317 | 2024 |
| Feature denoising for improving adversarial robustness C Xie, Y Wu, L Maaten, AL Yuille, K He Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 1058 | 2019 |
| Multi-scale dense networks for resource efficient image classification G Huang, D Chen, T Li, F Wu, L Van Der Maaten, KQ Weinberger arXiv preprint arXiv:1703.09844, 2017 | 1051* | 2017 |
| Condensenet: An efficient densenet using learned group convolutions G Huang, S Liu, L Van der Maaten, KQ Weinberger Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 1018 | 2018 |
| Learning a parametric embedding by preserving local structure L van der Maaten Proceedings of AI-STATS, 2009 | 753 | 2009 |
| Inferring and executing programs for visual reasoning J Johnson, B Hariharan, L Van Der Maaten, J Hoffman, L Fei-Fei, ... Proceedings of the IEEE international conference on computer vision, 2989-2998, 2017 | 650 | 2017 |
| Submanifold sparse convolutional networks B Graham, L Van der Maaten arXiv preprint arXiv:1706.01307, 2017 | 554 | 2017 |
| Convolutional networks with dense connectivity G Huang, Z Liu, G Pleiss, L Van Der Maaten, KQ Weinberger IEEE transactions on pattern analysis and machine intelligence 44 (12), 8704 …, 2019 | 551 | 2019 |
| Rtsne: T-distributed stochastic neighbor embedding using a Barnes-Hut implementation J Krijthe CRAN: Contributed Packages, 2014 | 548* | 2014 |
| Learning Visual Features from Large Weakly Supervised Data A Joulin, L van der Maaten, A Jabri, N Vasilache European Conference on Computer Vision, 2016 | 455 | 2016 |
| Certified data removal from machine learning models C Guo, T Goldstein, A Hannun, L Van Der Maaten arXiv preprint arXiv:1911.03030, 2019 | 434 | 2019 |
Kilian WeinbergerCornell University在 cornell.edu 的电子邮件经过验证
