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Abhishek Kumar
Abhishek Kumar
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Title
Cited by
Cited by
Year
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
38622020
Co-regularized multi-view spectral clustering
A Kumar, P Rai, H Daume
Advances in neural information processing systems (NIPS), 1413-1421, 2011
13782011
A co-training approach for multi-view spectral clustering
A Kumar, H Daumé
Proceedings of the 28th international conference on machine learning (ICML …, 2011
9732011
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
8422012
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
6042012
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
5442018
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
A Kumar, P Sattigeri, A Balakrishnan
International Conference on Learning Representations (ICLR), 2018
5412018
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
5172019
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
4712017
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
4242018
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
4032019
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
2402010
Co-regularization based semi-supervised domain adaptation
A Kumar, A Saha, H Daume
Advances in neural information processing systems (NIPS), 478-486, 2010
2132010
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
1912018
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
1882017
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
1842013
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
1812018
Weakly supervised disentanglement with guarantees
R Shu, Y Chen, A Kumar, S Ermon, B Poole
International Conference on Learning Representations. 2020., 2020
1472020
Robust Non-Negative Matrix Factorization under Separability Assumption
A Kumar, V Sindhwani
Handbook of robust low-rank and sparse matrix decomposition: Applications in …, 2016
126*2016
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
107*2022
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Articles 1–20