| Flow straight and fast: Learning to generate and transfer data with rectified flow X Liu, C Gong, Q Liu arXiv preprint arXiv:2209.03003, 2022 | 368 | 2022 |
| Conflict-averse gradient descent for multi-task learning B Liu, X Liu, X Jin, P Stone, Q Liu Advances in Neural Information Processing Systems 34, 18878-18890, 2021 | 267 | 2021 |
| Certified monotonic neural networks X Liu, X Han, N Zhang, Q Liu Advances in Neural Information Processing Systems 33, 15427-15438, 2020 | 99 | 2020 |
| Diffusion-based molecule generation with informative prior bridges L Wu, C Gong, X Liu, M Ye, Q Liu Advances in Neural Information Processing Systems 35, 36533-36545, 2022 | 92 | 2022 |
| Let us build bridges: Understanding and extending diffusion generative models X Liu, L Wu, M Ye, Q Liu arXiv preprint arXiv:2208.14699, 2022 | 91* | 2022 |
| Instaflow: One step is enough for high-quality diffusion-based text-to-image generation X Liu, X Zhang, J Ma, J Peng The Twelfth International Conference on Learning Representations, 2023 | 89 | 2023 |
| 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 | 72 | 2021 |
| Automl-gpt: Automatic machine learning with gpt S Zhang, C Gong, L Wu, X Liu, M Zhou arXiv preprint arXiv:2305.02499, 2023 | 66 | 2023 |
| Post-training quantization with multiple points: Mixed precision without mixed precision X Liu, M Ye, D Zhou, Q Liu Proceedings of the AAAI conference on artificial intelligence 35 (10), 8697-8705, 2021 | 42 | 2021 |
| Profiling pareto front with multi-objective stein variational gradient descent X Liu, X Tong, Q Liu Advances in Neural Information Processing Systems 34, 14721-14733, 2021 | 39 | 2021 |
| A langevin-like sampler for discrete distributions R Zhang, X Liu, Q Liu International Conference on Machine Learning, 26375-26396, 2022 | 36 | 2022 |
| Bi-objective trade-off with dynamic barrier gradient descent C Gong, X Liu NeurIPS 2021, 2021 | 36* | 2021 |
| Allsh: Active learning guided by local sensitivity and hardness S Zhang, C Gong, X Liu, P He, W Chen, M Zhou arXiv preprint arXiv:2205.04980, 2022 | 34 | 2022 |
| Centroid transformers: Learning to abstract with attention L Wu, X Liu, Q Liu arXiv preprint arXiv:2102.08606, 2021 | 31 | 2021 |
| Fast point cloud generation with straight flows L Wu, D Wang, C Gong, X Liu, Y Xiong, R Ranjan, R Krishnamoorthi, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 28 | 2023 |
| Sampling with trusthworthy constraints: A variational gradient framework X Liu, X Tong, Q Liu Advances in Neural Information Processing Systems 34, 23557-23568, 2021 | 12 | 2021 |
| Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding Z Li, J Zhang, Q Lin, J Xiong, Y Long, X Deng, Y Zhang, X Liu, M Huang, ... arXiv preprint arXiv:2405.08748, 2024 | 11 | 2024 |
| Flowgrad: Controlling the output of generative odes with gradients X Liu, L Wu, S Zhang, C Gong, W Ping, Q Liu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 9 | 2023 |
| Language rectified flow: Advancing diffusion language generation with probabilistic flows S Zhang, L Wu, C Gong, X Liu arXiv preprint arXiv:2403.16995, 2024 | 1 | 2024 |
| DISCS: a benchmark for discrete sampling K Goshvadi, H Sun, X Liu, A Nova, R Zhang, W Grathwohl, D Schuurmans, ... Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
Qiang LiuAssociate Professor of Computer Science, UT AustinVerified email at cs.utexas.edu
