| Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms D Shen, G Wang, W Wang, MR Min, Q Su, Y Zhang, C Li, R Henao, ... arXiv preprint arXiv:1805.09843, 2018 | 413 | 2018 |
| Deconvolutional latent-variable model for text sequence matching D Shen, Y Zhang, R Henao, Q Su, L Carin Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 87 | 2018 |
| Symmetric variational autoencoder and connections to adversarial learning L Chen, S Dai, Y Pu, E Zhou, C Li, Q Su, C Chen, L Carin International Conference on Artificial Intelligence and Statistics, 661-669, 2018 | 79 | 2018 |
| On convergence conditions of Gaussian belief propagation Q Su, YC Wu IEEE Transactions on Signal Processing 63 (5), 1144-1155, 2015 | 73 | 2015 |
| Unsupervised Hashing with Contrastive Information Bottleneck Z Qiu, Q Su, Z Ou, J Yu, C Chen Proceedings of the International Joint Conference on Artificial Intelligence …, 2021 | 64 | 2021 |
| Nash: Toward end-to-end neural architecture for generative semantic hashing D Shen, Q Su, P Chapfuwa, W Wang, G Wang, L Carin, R Henao Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 62 | 2018 |
| Syntax-enhanced pre-trained model Z Xu, D Guo, D Tang, Q Su, L Shou, M Gong, W Zhong, X Quan, N Duan, ... Proceedings of the 59th Annual Meeting of the Association for Computational …, 2020 | 48 | 2020 |
| Scalable bayesian learning of recurrent neural networks for language modeling Z Gan, C Li, C Chen, Y Pu, Q Su, L Carin Proceedings of the 55th Annual Meeting of the Association for Computational …, 2016 | 44 | 2016 |
| Convergence analysis of the variance in Gaussian belief propagation Q Su, YC Wu IEEE Transactions on Signal Processing 62 (19), 5119-5131, 2014 | 34 | 2014 |
| A convergence analysis for a class of practical variance-reduction stochastic gradient MCMC C Chen, W Wang, Y Zhang, Q Su, L Carin Science China Information Sciences 62, 1-13, 2019 | 33 | 2019 |
| A distributed dynamic spectrum access and power allocation algorithm for femtocell networks Q Su, A Huango, Z Wu, G Yu, Z Zhang, K Xu, J Yang 2009 International Conference on Wireless Communications & Signal Processing …, 2009 | 33 | 2009 |
| Constituency lattice encoding for aspect term extraction Y Yang, K Li, X Quan, W Shen, Q Su Proceedings of the 28th international conference on computational …, 2020 | 23 | 2020 |
| Generating multi-hop reasoning questions to improve machine reading comprehension J Yu, X Quan, Q Su, J Yin Proceedings of The Web Conference 2020, 281-291, 2020 | 22 | 2020 |
| Document hashing with mixture-prior generative models W Dong, Q Su, D Shen, C Chen Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 19 | 2019 |
| Nonlinear statistical learning with truncated gaussian graphical models Q Su, X Liao, C Chen, L Carin International Conference on Machine Learning, 1948-1957, 2016 | 19 | 2016 |
| A probabilistic framework for nonlinearities in stochastic neural networks Q Su, L Carin Advances in Neural Information Processing Systems 30, 2017 | 18 | 2017 |
| Embedding dynamic attributed networks by modeling the evolution processes Z Xu, Z Ou, Q Su, J Yu, X Quan, Z Lin Proceedings of the 28th International Conference on Computational …, 2020 | 16 | 2020 |
| Low-resource generation of multi-hop reasoning questions J Yu, W Liu, S Qiu, Q Su, K Wang, X Quan, J Yin Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 15 | 2020 |
| Unsupervised learning with truncated gaussian graphical models Q Su, X Liao, C Li, Z Gan, L Carin Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 13 | 2017 |
| Learning to answer psychological questionnaire for personality detection F Yang, T Yang, X Quan, Q Su Findings of the Association for Computational Linguistics: EMNLP 2021, 1131-1142, 2021 | 12 | 2021 |
Lawrence CarinDuke UniversityVerified email at duke.edu
