| Using Bayesian networks to discover relations between genes, environment, and disease C Su, A Andrew, MR Karagas, ME Borsuk BioData mining 6, 1-21, 2013 | 119 | 2013 |
| Towards an ASR error robust Spoken Language Understanding System W Ruan, Y Nechaev, L Chen, C Su, I Kiss | 66 | 2020 |
| ASR N-BEST FUSION NETS X Liu, M Li, L Chen, P Wanigasekara, W Ruan, H Khan, W Hamza, C Su, ... | 51 | 2021 |
| A re-ranker scheme for integrating large scale nlu models C Su, R Gupta, S Ananthakrishnan, S Matsoukas 2018 IEEE Spoken Language Technology Workshop (SLT), 670-676, 2018 | 39 | 2018 |
| Exploring transfer learning for end-to-end spoken language understanding S Rongali, B Liu, L Cai, K Arkoudas, C Su, W Hamza Proceedings of the AAAI Conference on Artificial Intelligence 35 (15), 13754 …, 2021 | 27 | 2021 |
| Improving spoken language understanding by exploiting asr n-best hypotheses M Li, W Ruan, X Liu, L Soldaini, W Hamza, C Su arXiv preprint arXiv:2001.05284, 2020 | 24 | 2020 |
| Improving structure mcmc for bayesian networks through markov blanket resampling C Su, ME Borsuk The Journal of Machine Learning Research 17 (1), 4042-4061, 2016 | 23 | 2016 |
| Contextual natural language processing C Su, S Ananthakrishnan, S Matsoukas, S Saleem, R Gupta, K Ravikumar, ... US Patent 11,081,104, 2021 | 11 | 2021 |
| Overview of Bayesian network approaches to model gene-environment interactions and cancer susceptibility C Su, A Andrew, M Karagas, ME Borsuk | 8 | 2012 |
| Multi-task learning of spoken language understanding by integrating n-best hypotheses with hierarchical attention M Li, X Liu, W Ruan, L Soldaini, W Hamza, C Su Proceedings of the 28th International Conference on Computational …, 2020 | 7 | 2020 |
| Incorporating prior expert knowledge in learning Bayesian networks from genetic epidemiological data C Su, ME Borsuk, A Andrew, M Karagas 2014 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2014 | 7 | 2014 |
| Semantic VL-BERT: Visual Grounding via Attribute Learning P Wanigasekara, K Qin, E Barut, F Yang, W Ruan, C Su | 5 | 2022 |
| Can small language models help large language models reason better?: LM-guided chain-of-thought J Lee, F Yang, T Tran, Q Hu, E Barut, KW Chang, C Su arXiv preprint arXiv:2404.03414, 2024 | 4 | 2024 |
| Multimodal context carryover P Wanigasekara, N Gupta, F Yang, E Barut, Z Raeesy, K Qin, S Rawls, ... Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 3 | 2022 |
| Natural language understanding processing C Su, S Matsoukas, S Ananthakrishnan, S Saleem, C Chan, Y Li, ... US Patent 11,335,346, 2022 | 3 | 2022 |
| Speech processing B Liu, W Hamza, L Cai, K Arkoudas, C Su, S Rongali US Patent 11,682,400, 2023 | 2 | 2023 |
| Introducing deep reinforcement learning to NLU ranking tasks G Yu, E Barut, C Su ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 2 | 2021 |
| Optimizing NLU reranking using entity resolution signals in multi-domain dialog systems T Wang, J Chen, M Malmir, S Dong, X He, H Wang, C Su, Y Liu, Y Liu Proceedings of the 2021 Conference of the North American Chapter of the …, 2021 | 2 | 2021 |
| Speech processing B Liu, W Hamza, L Cai, K Arkoudas, C Su, S Rongali US Patent 12,087,305, 2024 | 1 | 2024 |
| Masking latent gender knowledge for debiasing image captioning F Yang, S Ghosh, E Barut, K Qin, P Wanigasekara, C Su, W Ruan, ... Proceedings of the 4th Workshop on Trustworthy Natural Language Processing …, 2024 | 1 | 2024 |
