| Geodesic flow kernel for unsupervised domain adaptation B Gong, Y Shi, F Sha, K Grauman 2012 IEEE conference on computer vision and pattern recognition, 2066-2073, 2012 | 2934 | 2012 |
| Shallow parsing with conditional random fields F Sha, F Pereira Proceedings of the 2003 human language technology conference of the North …, 2003 | 1968 | 2003 |
| Marginalized denoising auto-encoders for nonlinear representations M Chen, K Weinberger, F Sha, Y Bengio International conference on machine learning, 1476-1484, 2014 | 1113 | 2014 |
| Actor-attention-critic for multi-agent reinforcement learning S Iqbal, F Sha International conference on machine learning, 2961-2970, 2019 | 959 | 2019 |
| Synthesized classifiers for zero-shot learning S Changpinyo, WL Chao, B Gong, F Sha Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 948 | 2016 |
| Video summarization with long short-term memory K Zhang, WL Chao, F Sha, K Grauman Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 895 | 2016 |
| Few-shot learning via embedding adaptation with set-to-set functions HJ Ye, H Hu, DC Zhan, F Sha Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 892 | 2020 |
| An empirical study and analysis of generalized zero-shot learning for object recognition in the wild WL Chao, S Changpinyo, B Gong, F Sha Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 703 | 2016 |
| Learning a kernel matrix for nonlinear dimensionality reduction KQ Weinberger, F Sha, LK Saul Proceedings of the twenty-first international conference on Machine learning …, 2004 | 697 | 2004 |
| Diverse sequential subset selection for supervised video summarization B Gong, WL Chao, K Grauman, F Sha Advances in neural information processing systems 27, 2014 | 555 | 2014 |
| DiscLDA: Discriminative learning for dimensionality reduction and classification S Lacoste-Julien, F Sha, M Jordan Advances in neural information processing systems 21, 2008 | 552 | 2008 |
| Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation B Gong, K Grauman, F Sha International conference on machine learning, 222-230, 2013 | 505 | 2013 |
| Learning globally-consistent local distance functions for shape-based image retrieval and classification A Frome, Y Singer, F Sha, J Malik 2007 IEEE 11th international conference on computer vision, 1-8, 2007 | 486 | 2007 |
| Learning with whom to share in multi-task feature learning Z Kang, K Grauman, F Sha Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 467 | 2011 |
| Multiplicative updates for nonnegative quadratic programming in support vector machines F Sha, L Saul, D Lee Advances in neural information processing systems 15, 2002 | 395 | 2002 |
| Spectral methods for dimensionality reduction LK Saul, KQ Weinberger, F Sha, J Ham, DD Lee | 364 | 2006 |
| Deformable spatial pyramid matching for fast dense correspondences J Kim, C Liu, F Sha, K Grauman Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 292 | 2013 |
| Information-theoretical learning of discriminative clusters for unsupervised domain adaptation Y Shi, F Sha arXiv preprint arXiv:1206.6438, 2012 | 283 | 2012 |
| Non-linear metric learning D Kedem, S Tyree, F Sha, G Lanckriet, KQ Weinberger Advances in neural information processing systems 25, 2012 | 278 | 2012 |
| Summary transfer: Exemplar-based subset selection for video summarization K Zhang, WL Chao, F Sha, K Grauman Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 270 | 2016 |
Kristen GraumanProfessor of Computer Science, University of Texas at AustinVerified email at cs.utexas.edu
