| Stein variational gradient descent: A general purpose bayesian inference algorithm Q Liu, D Wang Advances in neural information processing systems (NeurIPS), 2378-2386, 2016 | 857 | 2016 |
| Learning to draw samples: With application to amortized mle for generative adversarial learning D Wang, Q Liu arXiv preprint arXiv:1611.01722, 2016 | 179 | 2016 |
| An Optimization View on Dynamic Routing Between Capsules D Wang, Q Liu Workshop track - ICLR 2018, 2018 | 100 | 2018 |
| Entity disambiguation by knowledge and text jointly embedding W Fang, J Zhang, D Wang, Z Chen, M Li Proceedings of the 20th SIGNLL conference on computational natural language …, 2016 | 94 | 2016 |
| Improving neural language modeling via adversarial training D Wang, C Gong, Q Liu Proceedings of the 36th International Conference on Machine Learning (ICML), 2019 | 91 | 2019 |
| KeepAugment: A Simple Information-Preserving Data Augmentation Approach C Gong, D Wang, M Li, V Chandra, Q Liu Conference on Computer Vision and Pattern Recognition (CVPR) 2021, 2020 | 73 | 2020 |
| Learning to draw samples with amortized stein variational gradient descent Y Feng, D Wang, Q Liu Conference on Uncertainty in Artificial Intelligence (UAI), 2017 | 71 | 2017 |
| Multi-scale high-resolution vision transformer for semantic segmentation J Gu, H Kwon, D Wang, W Ye, M Li, YH Chen, L Lai, V Chandra, DZ Pan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 62* | 2022 |
| AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling D Wang, M Li, C Gong, V Chandra Conference on Computer Vision and Pattern Recognition (CVPR) 2021, 2020 | 59 | 2020 |
| Mixed Precision Neural Architecture Search for Energy Efficient Deep Learning C Gong, Z Jiang, D Wang, Y Lin, Q Liu, DZ Pan International Conference On Computer Aided Design (ICCAD), 1-7, 2019 | 53 | 2019 |
| Stein variational gradient descent with matrix-valued kernels D Wang, Z Tang, C Bajaj, Q Liu Advances in neural information processing systems (NeurIPS), 7836-7846, 2019 | 53 | 2019 |
| Stein Variational Message Passing for Continuous Graphical Models D Wang, Z Zeng, Q Liu International Conference on Machine Learning (ICML), 2017 | 49 | 2017 |
| Variational inference with tail-adaptive f-divergence D Wang, H Liu, Q Liu Advances in Neural Information Processing Systems (NeurIPS), 5737-5747, 2018 | 48 | 2018 |
| Vision Transformers with Patch Diversification C Gong, D Wang, M Li, V Chandra, Q Liu arXiv preprint arXiv:2104.12753, 2021 | 47* | 2021 |
| AlphaNet: Improved Training of Supernets with Alpha-Divergence D Wang, C Gong, M Li, Q Liu, V Chandra International Conference on Machine Learning (ICML) 2021, 2021 | 41 | 2021 |
| Stein variational gradient descent as moment matching Q Liu, D Wang Advances in Neural Information Processing Systems (NeurIPS), 8854-8863, 2018 | 41 | 2018 |
| AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence C Gong, D Wang, Q Liu Conference on Computer Vision and Pattern Recognition (CVPR) 2021, 2020 | 32 | 2020 |
| Splitting steepest descent for growing neural architectures Q Liu, L Wu, D Wang Advances in Neural Information Processing Systems (NeurIPS), 10656-10666, 2019 | 30 | 2019 |
| 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, 2021 | 24* | 2021 |
| Nonlinear stein variational gradient descent for learning diversified mixture models D Wang, Q Liu International Conference on Machine Learning (ICML), 6576-6585, 2019 | 22 | 2019 |
Qiang LiuAssistant Professor of Computer Science, UT AustinVerified email at cs.utexas.edu
