| Image to image translation for domain adaptation Z Murez, S Kolouri, D Kriegman, R Ramamoorthi, K Kim Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 910 | 2018 |
| Explainability methods for graph convolutional neural networks PE Pope, S Kolouri, M Rostami, CE Martin, H Hoffmann Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 668 | 2019 |
| Optimal mass transport: Signal processing and machine-learning applications S Kolouri, SR Park, M Thorpe, D Slepcev, GK Rohde IEEE signal processing magazine 34 (4), 43-59, 2017 | 571 | 2017 |
| Generalized sliced Wasserstein distances S Kolouri, K Nadjahi, U Simsekli, R Badeau, GK Rohde Neural Information Processing Systems, 2019 | 358 | 2019 |
| Sliced Wasserstein auto-encoders S Kolouri, PE Pope, CE Martin, GK Rohde International Conference on Learning Representations, 2019 | 311* | 2019 |
| Universal litmus patterns: Revealing backdoor attacks in cnns S Kolouri, A Saha, H Pirsiavash, H Hoffmann Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 271 | 2020 |
| Deep transfer learning for few-shot SAR image classification M Rostami, S Kolouri, E Eaton, K Kim Remote Sensing 11 (11), 1374, 2019 | 245 | 2019 |
| Biological underpinnings for lifelong learning machines D Kudithipudi, M Aguilar-Simon, J Babb, M Bazhenov, D Blackiston, ... Nature Machine Intelligence 4 (3), 196-210, 2022 | 238 | 2022 |
| Sliced Wasserstein kernels for probability distributions S Kolouri, Y Zou, GK Rohde Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 209 | 2016 |
| Sliced Wasserstein Distance for Learning Gaussian Mixture Models S Kolouri, GK Rohde, H Hoffmann IEEE Conference on Computer vision and pattern recognition, 3427-3436, 2018 | 167 | 2018 |
| Transport-based single frame super resolution of very low resolution face images S Kolouri, GK Rohde Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 117 | 2015 |
| The radon cumulative distribution transform and its application to image classification S Kolouri, SR Park, GK Rohde IEEE transactions on image processing 25 (2), 920-934, 2015 | 104 | 2015 |
| Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry S Basu, S Kolouri, GK Rohde Proceedings of the National Academy of Sciences 111 (9), 3448-3453, 2014 | 103 | 2014 |
| Wasserstein embedding for graph learning S Kolouri, N Naderializadeh, GK Rohde, H Hoffmann International Conference on Learning Representations (ICLR), 2021 | 102 | 2021 |
| Statistical and topological properties of sliced probability divergences K Nadjahi, A Durmus, L Chizat, S Kolouri, S Shahrampour, U Şimşekli Neural Information Processing Systems, 2020 | 99 | 2020 |
| Accurate diagnosis of thyroid follicular lesions from nuclear morphology using supervised learning JA Ozolek, AB Tosun, W Wang, C Chen, S Kolouri, S Basu, H Huang, ... Medical image analysis 18 (5), 772-780, 2014 | 92 | 2014 |
| Complementary learning for overcoming catastrophic forgetting using experience replay M Rostami, S Kolouri, PK Pilly Proceedings of the Twenty-Eighth International Joint Conference on …, 2019 | 88 | 2019 |
| A Transportation Distance for Signal Analysis M Thorpe, S Park, S Kolouri, GK Rohde, D Slepčev Journal of mathematical imaging and vision 59, 187-210, 2017 | 85 | 2017 |
| Gat: Generative adversarial training for adversarial example detection and robust classification X Yin, S Kolouri, GK Rohde arXiv preprint arXiv:1905.11475, 2019 | 82* | 2019 |
| The cumulative distribution transform and linear pattern classification SR Park, S Kolouri, S Kundu, GK Rohde Applied and computational harmonic analysis 45 (3), 616-641, 2018 | 81 | 2018 |
Gustavo Kunde RohdeUniversity of Virginia確認したメール アドレス: virginia.edu
