| Score-based generative modeling through stochastic differential equations Y Song, J Sohl-Dickstein, DP Kingma, A Kumar, S Ermon, B Poole arXiv preprint arXiv:2011.13456, 2020 | 6188 | 2020 |
| Co-regularized multi-view spectral clustering A Kumar, P Rai, H Daume Advances in neural information processing systems 24, 2011 | 1466 | 2011 |
| A co-training approach for multi-view spectral clustering A Kumar, H Daumé Proceedings of the 28th international conference on machine learning (ICML …, 2011 | 1013 | 2011 |
| Generalized multiview analysis: A discriminative latent space A Sharma, A Kumar, H Daume, DW Jacobs 2012 IEEE conference on computer vision and pattern recognition, 2160-2167, 2012 | 808 | 2012 |
| Learning task grouping and overlap in multi-task learning A Kumar, H Daume III Proceedings of the 29th International Coference on Machine Learning (ICML), 2012 | 625 | 2012 |
| Spottune: transfer learning through adaptive fine-tuning Y Guo, H Shi, A Kumar, K Grauman, T Rosing, R Feris Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 604 | 2019 |
| Variational Inference of Disentangled Latent Concepts from Unlabeled Observations A Kumar, P Sattigeri, A Balakrishnan International Conference on Learning Representations (ICLR), 2018 | 597 | 2018 |
| BlockDrop: Dynamic Inference Paths in Residual Networks Z Wu, T Nagarajan, A Kumar, S Rennie, LS Davis, K Grauman, R Feris Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 596 | 2018 |
| Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification Y Lu, A Kumar, S Zhai, Y Cheng, T Javidi, R Feris Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 504 | 2017 |
| Delta-encoder: an effective sample synthesis method for few-shot object recognition E Schwartz, L Karlinsky, J Shtok, S Harary, M Marder, R Feris, A Kumar, ... Advances in Neural Information Processing Systems 31 (2018), 2018 | 465 | 2018 |
| Repmet: Representative-based metric learning for classification and few-shot object detection L Karlinsky, J Shtok, S Harary, E Schwartz, A Aides, R Feris, R Giryes, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 442 | 2019 |
| Frustratingly easy semi-supervised domain adaptation H Daumé III, A Kumar, A Saha Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language …, 2010 | 241 | 2010 |
| Co-regularization based semi-supervised domain adaptation A Kumar, A Saha, H Daume Advances in neural information processing systems (NIPS), 478-486, 2010 | 221 | 2010 |
| Co-regularized alignment for unsupervised domain adaptation A Kumar, P Sattigeri, K Wadhawan, L Karlinsky, R Feris, WT Freeman, ... Advances in Neural Information Processing Systems 31 (2018): 9345-9356., 2018 | 201 | 2018 |
| Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference A Kumar, P Sattigeri, T Fletcher Advances in Neural Information Processing Systems (NIPS), 5540-5550, 2017 | 197 | 2017 |
| The riemannian geometry of deep generative models H Shao, A Kumar, P Thomas Fletcher Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 195 | 2018 |
| Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization A Kumar, V Sindhwani, P Kambadur Proceedings of the 30th International Conference on Machine Learning (ICML), 2013 | 182 | 2013 |
| Weakly supervised disentanglement with guarantees R Shu, Y Chen, A Kumar, S Ermon, B Poole International Conference on Learning Representations. 2020., 2020 | 163 | 2020 |
| Diffusevae: Efficient, controllable and high-fidelity generation from low-dimensional latents K Pandey, A Mukherjee, P Rai, A Kumar Transactions on Machine Learning Research (TMLR), 2022, 2022 | 144* | 2022 |
| Robust Non-Negative Matrix Factorization under Separability Assumption A Kumar, V Sindhwani Handbook of robust low-rank and sparse matrix decomposition: Applications in …, 2016 | 136* | 2016 |
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandПотвърден имейл адрес: umiacs.umd.edu