| Making deep neural networks robust to label noise: A loss correction approach G Patrini, A Rozza, A Krishna Menon, R Nock, L Qu Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1183 | 2017 |
| Stranse: a novel embedding model of entities and relationships in knowledge bases DQ Nguyen, K Sirts, L Qu, M Johnson arXiv preprint arXiv:1606.08140, 2016 | 218 | 2016 |
| The bag-of-opinions method for review rating prediction from sparse text patterns L Qu, G Ifrim, G Weikum Proceedings of the 23rd International Conference on Computational …, 2010 | 216 | 2010 |
| Timely yago: harvesting, querying, and visualizing temporal knowledge from wikipedia Y Wang, M Zhu, L Qu, M Spaniol, G Weikum Proceedings of the 13th international conference on extending database …, 2010 | 135 | 2010 |
| Automatic generation of grounded visual questions S Zhang, L Qu, S You, Z Yang, J Zhang arXiv preprint arXiv:1612.06530, 2016 | 70 | 2016 |
| Harvesting facts from textual web sources by constrained label propagation Y Wang, B Yang, L Qu, M Spaniol, G Weikum Proceedings of the 20th ACM international conference on Information and …, 2011 | 63 | 2011 |
| Neighborhood mixture model for knowledge base completion DQ Nguyen, K Sirts, L Qu, M Johnson arXiv preprint arXiv:1606.06461, 2016 | 55 | 2016 |
| Chord2vec: Learning musical chord embeddings S Madjiheurem, L Qu, C Walder Proceedings of the constructive machine learning workshop at 30th conference …, 2016 | 46 | 2016 |
| Named entity recognition for novel types by transfer learning L Qu, G Ferraro, L Zhou, W Hou, T Baldwin arXiv preprint arXiv:1610.09914, 2016 | 44 | 2016 |
| CD-VulD: Cross-domain vulnerability discovery based on deep domain adaptation S Liu, G Lin, L Qu, J Zhang, O De Vel, P Montague, Y Xiang IEEE Transactions on Dependable and Secure Computing 19 (1), 438-451, 2020 | 43 | 2020 |
| Big data small data, in domain out-of domain, known word unknown word: The impact of word representation on sequence labelling tasks L Qu, G Ferraro, L Zhou, W Hou, N Schneider, T Baldwin arXiv preprint arXiv:1504.05319, 2015 | 40 | 2015 |
| Demographic inference on twitter using recursive neural networks S Mac Kim, Q Xu, L Qu, S Wan, C Paris Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017 | 37 | 2017 |
| A weakly supervised model for sentence-level semantic orientation analysis with multiple experts L Qu, R Gemulla, G Weikum Proceedings of the 2012 Joint Conference on Empirical Methods in Natural …, 2012 | 35 | 2012 |
| D-page: Diverse paraphrase generation Q Xu, J Zhang, L Qu, L Xie, R Nock arXiv preprint arXiv:1808.04364, 2018 | 32 | 2018 |
| Maximal divergence sequential autoencoder for binary software vulnerability detection T Le, T Nguyen, T Le, D Phung, P Montague, O De Vel, L Qu International Conference on Learning Representations, 2019 | 31 | 2019 |
| Easy-to-deploy API extraction by multi-level feature embedding and transfer learning S Ma, Z Xing, C Chen, C Chen, L Qu, G Li IEEE Transactions on Software Engineering 47 (10), 2296-2311, 2019 | 30 | 2019 |
| Deep domain adaptation for vulnerable code function identification V Nguyen, T Le, T Le, K Nguyen, O DeVel, P Montague, L Qu, D Phung 2019 international joint conference on neural networks (IJCNN), 1-8, 2019 | 28 | 2019 |
| Privacy-aware text rewriting Q Xu, L Qu, C Xu, R Cui Proceedings of the 12th International Conference on Natural Language …, 2019 | 28 | 2019 |
| Adhering, steering, and queering: Treatment of gender in natural language generation Y Strengers, L Qu, Q Xu, J Knibbe Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems …, 2020 | 21 | 2020 |
| Sentence Level Subjectivity and Sentiment Analysis Experiments in NTCIR-7 MOAT Challenge. L Qu, C Toprak, N Jakob, I Gurevych NTCIR, 2008 | 18 | 2008 |
Qiongkai XuThe University of MelbourneVerified email at unimelb.edu.au
