| Back to the future: Radial basis function networks revisited Q Que, M Belkin Artificial intelligence and statistics, 1375-1383, 2016 | 102 | 2016 |
| Revisiting kernelized locality-sensitive hashing for improved large-scale image retrieval K Jiang, Q Que, B Kulis Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 62 | 2015 |
| Feature‐preserving reconstruction of singular surfaces TK Dey, X Ge, Q Que, I Safa, L Wang, Y Wang Computer Graphics Forum 31 (5), 1787-1796, 2012 | 38 | 2012 |
| Toward understanding complex spaces: Graph laplacians on manifolds with singularities and boundaries M Belkin, Q Que, Y Wang, X Zhou Conference on learning theory, 36.1-36.26, 2012 | 34 | 2012 |
| Inverse density as an inverse problem: The fredholm equation approach Q Que, M Belkin Advances in neural information processing systems 26, 2013 | 24 | 2013 |
| Learning with Fredholm kernels Q Que, M Belkin, Y Wang Advances in neural information processing systems 27, 2014 | 11 | 2014 |
| Feature selection for Facebook feed ranking system via a group-sparsity-regularized training algorithm X Ni, Y Yu, P Wu, Y Li, S Nie, Q Que, C Chen Proceedings of the 28th acm international conference on information and …, 2019 | 6 | 2019 |
| Graph laplacians on singular manifolds: Toward understanding complex spaces: Graph laplacians on manifolds with singularities and boundaries M Belkin, Q Que, Y Wang, X Zhou arXiv preprint arXiv:1211.6727, 2012 | 5 | 2012 |
| Integral Equations For Machine Learning Problems Q Que The Ohio State University, 2016 | 1 | 2016 |
| Supplementary Material: Inverse Density as an Inverse Problem Q Que, M Belkin | | |
