| Neural User Response Generator: Fake News Detection with Collective User Intelligence. F Qian, C Gong, K Sharma, Y Liu IJCAI 18, 3834-3840, 2018 | 178 | 2018 |
| Frage: Frequency-agnostic word representation C Gong, D He, X Tan, T Qin, L Wang, TY Liu Advances in neural information processing systems 31, 2018 | 143 | 2018 |
| Maxup: A simple way to improve generalization of neural network training C Gong, T Ren, M Ye, Q Liu arXiv preprint arXiv:2002.09024, 2020 | 90* | 2020 |
| Improving neural language modeling via adversarial training D Wang, C Gong, Q Liu International Conference on Machine Learning, 6555-6565, 2019 | 86 | 2019 |
| Good subnetworks provably exist: Pruning via greedy forward selection M Ye, C Gong, L Nie, D Zhou, A Klivans, Q Liu International Conference on Machine Learning, 10820-10830, 2020 | 75 | 2020 |
| Keepaugment: A simple information-preserving data augmentation approach C Gong, D Wang, M Li, V Chandra, Q Liu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 60 | 2021 |
| SAFER: A structure-free approach for certified robustness to adversarial word substitutions M Ye, C Gong, Q Liu arXiv preprint arXiv:2005.14424, 2020 | 54 | 2020 |
| Attentivenas: Improving neural architecture search via attentive sampling D Wang, M Li, C Gong, V Chandra Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 53 | 2021 |
| Mixed precision neural architecture search for energy efficient deep learning C Gong, Z Jiang, D Wang, Y Lin, Q Liu, DZ Pan 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 1-7, 2019 | 47 | 2019 |
| Black-box certification with randomized smoothing: A functional optimization based framework D Zhang, M Ye, C Gong, Z Zhu, Q Liu Advances in Neural Information Processing Systems 33, 2316-2326, 2020 | 44 | 2020 |
| Improve vision transformers training by suppressing over-smoothing C Gong, D Wang, M Li, V Chandra, Q Liu arXiv preprint arXiv:2104.12753 4 (11), 2021 | 41* | 2021 |
| Alphanet: Improved training of supernets with alpha-divergence D Wang, C Gong, M Li, Q Liu, V Chandra International Conference on Machine Learning, 10760-10771, 2021 | 36 | 2021 |
| Quantile stein variational gradient descent for batch Bayesian optimization C Gong, J Peng, Q Liu International Conference on Machine Learning, 2347-2356, 2019 | 33 | 2019 |
| Fusedream: Training-free text-to-image generation with improved clip+ gan space optimization X Liu, C Gong, L Wu, S Zhang, H Su, Q Liu arXiv preprint arXiv:2112.01573, 2021 | 29 | 2021 |
| Alphamatch: Improving consistency for semi-supervised learning with alpha-divergence C Gong, D Wang, Q Liu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 26 | 2021 |
| Knowing more about questions can help: Improving calibration in question answering S Zhang, C Gong, E Choi arXiv preprint arXiv:2106.01494, 2021 | 20 | 2021 |
| NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training C Gong, D Wang, M Li, X Chen, Z Yan, Y Tian, V Chandra International Conference on Learning Representations, 2022 | 17* | 2022 |
| Capturing label distribution: A case study in nli S Zhang, C Gong, E Choi arXiv preprint arXiv:2102.06859, 2021 | 16* | 2021 |
| Automatic and harmless regularization with constrained and lexicographic optimization: A dynamic barrier approach C Gong, X Liu, Q Liu Advances in Neural Information Processing Systems 34, 29630-29642, 2021 | 13* | 2021 |
| Deep dynamic Poisson factorization model C Gong Advances in Neural Information Processing Systems 30, 2017 | 13 | 2017 |
Qiang LiuAssistant Professor of Computer Science, UT AustinVerified email at cs.utexas.edu
