| Efficient mining of frequent subgraphs in the presence of isomorphism J Huan, W Wang, J Prins Third IEEE international conference on data mining, 549-552, 2003 | 887 | 2003 |
| The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models L Shi, G Campbell, WD Jones, F Campagne, Z Wen, SJ Walker, Z Su, ... Nature biotechnology 28 (8), 827, 2010 | 789 | 2010 |
| Spin: mining maximal frequent subgraphs from graph databases J Huan, W Wang, J Prins, J Yang Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 476 | 2004 |
| Graph database indexing using structured graph decomposition DW Williams, J Huan, W Wang 2007 IEEE 23rd International Conference on Data Engineering, 976-985, 2006 | 274 | 2006 |
| UTS: An unbalanced tree search benchmark S Olivier, J Huan, J Liu, J Prins, J Dinan, P Sadayappan, CW Tseng Languages and Compilers for Parallel Computing: 19th International Workshop …, 2007 | 238 | 2007 |
| Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data A Koutsoukas, KJ Monaghan, X Li, J Huan Journal of cheminformatics 9 (1), 1-13, 2017 | 230 | 2017 |
| Mining protein family specific residue packing patterns from protein structure graphs J Huan, W Wang, D Bandyopadhyay, J Snoeyink, J Prins, A Tropsha Proceedings of the eighth annual international conference on Research in …, 2004 | 179 | 2004 |
| Large margin transductive transfer learning B Quanz, J Huan Proceedings of the 18th ACM conference on Information and knowledge …, 2009 | 153 | 2009 |
| Comparing graph representations of protein structure for mining family-specific residue-based packing motifs J Huan, D Bandyopadhyay, W Wang, J Snoeyink, J Prins, A Tropsha Journal of Computational Biology 12 (6), 657-671, 2005 | 127 | 2005 |
| DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks X Li, H Xiong, H Wang, Y Rao, L Liu, J Huan The Seventh International Conference on Learning Representations (ICLR'19), 2019 | 105 | 2019 |
| Inductive multi-task learning with multiple view data J Zhang, J Huan Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012 | 105 | 2012 |
| Knowledge transfer with low-quality data: A feature extraction issue B Quanz, J Huan, M Mishra IEEE Transactions on Knowledge and Data Engineering 24 (10), 1789-1802, 2012 | 93 | 2012 |
| Accurate classification of protein structural families using coherent subgraph analysis J Huan, W Wang, A Washington, J Prins, R Shah, A Tropsha Biocomputing 2004, 411-422, 2003 | 84 | 2003 |
| An efficient graph-mining method for complicated and noisy data with real-world applications Y Jia, J Zhang, J Huan Knowledge and Information Systems 28, 423-447, 2011 | 81 | 2011 |
| Structured feature selection and task relationship inference for multi-task learning H Fei, J Huan Knowledge and information systems 35, 345-364, 2013 | 58 | 2013 |
| Anomaly localization for network data streams with graph joint sparse PCA R Jiang, H Fei, J Huan Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 57 | 2011 |
| On the noisy gradient descent that generalizes as sgd J Wu, W Hu, H Xiong, J Huan, V Braverman, Z Zhu International Conference on Machine Learning, 10367-10376, 2020 | 52 | 2020 |
| G-hash: towards fast kernel-based similarity search in large graph databases X Wang, A Smalter, J Huan, GH Lushington Proceedings of the 12th international conference on extending database …, 2009 | 52 | 2009 |
| Structurebased function inference using protein familyspecific fingerprints D Bandyopadhyay, J Huan, J Liu, J Prins, J Snoeyink, W Wang, ... Protein Science 15 (6), 1537-1543, 2006 | 52 | 2006 |
| Boosting with structure information in the functional space: an application to graph classification H Fei, J Huan Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010 | 51 | 2010 |
Wei WangLeonard Kleinrock Professor in Computer Science, UCLA在 cs.ucla.edu 的电子邮件经过验证
