| Visualizing data using t-SNE L van der Maaten, G Hinton The Journal of Machine Learning Research 9 (2579-2605), 85, 2008 | 19559 | 2008 |
| Densely Connected Convolutional Networks G Huang, Z Liu, L van der Maaten, KQ Weinberger IEEE Conference on Computer Vision and Pattern Recognition, 2016 | 16109 | 2016 |
| Dimensionality reduction: A comparative review LJP Van der Maaten, EO Postma, HJ Van den Herik Technical Report TiCC TR 2009-005, 2009 | 2584* | 2009 |
| Accelerating t-SNE using Tree-Based Algorithms L Van Der Maaten The Journal of Machine Learning Research 15 (1), 3221-3245, 2014 | 1669 | 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 | 916 | 2017 |
| Countering adversarial images using input transformations C Guo, M Rana, M Cisse, L Van Der Maaten arXiv preprint arXiv:1711.00117, 2017 | 609 | 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 | 553 | 2018 |
| Learning a parametric embedding by preserving local structure L van der Maaten Proceedings of AI-STATS, 2009 | 439 | 2009 |
| 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 | 424 | 2018 |
| 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 | 359 | 2017 |
| 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 | 339* | 2017 |
| 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 | 335 | 2019 |
| 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 | 328 | 2018 |
| Rtsne: T-distributed stochastic neighbor embedding using Barnes-Hut implementation JH Krijthe, L Van der Maaten R package version 0.13, URL https://github. com/jkrijthe/Rtsne, 2015 | 246 | 2015 |
| Self-supervised learning of pretext-invariant representations I Misra, L Maaten Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 232 | 2020 |
| Revisiting visual question answering baselines A Jabri, A Joulin, L Van Der Maaten European conference on computer vision, 727-739, 2016 | 226 | 2016 |
| Structure Preserving Object Tracking L Zhang, L van der Maaten IEEE Conference on Computer Vision and Pattern Recognition, 2013 | 217 | 2013 |
| Learning Visual Features from Large Weakly Supervised Data A Joulin, L van der Maaten, A Jabri, N Vasilache European Conference on Computer Vision, 2016 | 215 | 2016 |
| Barnes-Hut-SNE L van der Maaten International Conference on Learning Representations / arXiv preprint arXiv …, 2013 | 197 | 2013 |
| Visualizing non-metric similarities in multiple maps L van der Maaten, G Hinton Machine Learning, 1-23, 2012 | 196 | 2012 |
Kilian WeinbergerCornell University在 cornell.edu 的电子邮件经过验证
