| Predicting Physics in Mesh-reduced Space with Temporal Attention X Han, H Gao, T Pfaff, JX Wang, LP Liu International Conference on Learning Representations (ICLR 2022), 2022 | 75 | 2022 |
| GAN Ensemble for Anomaly Detection X Han, X Chen, LP Liu AAAI Conference on Artificial Intelligence (AAAI 2021), 2020 | 74 | 2020 |
| Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling X Chen, J He, X Han, LP Liu International Conference on Machine Learning (ICML 2023), 2023 | 33 | 2023 |
| Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation X Chen, X Han, J Hu, FJR Ruiz, L Liu International Conference on Machine Learning (ICML 2021), 2021 | 31 | 2021 |
| Fitting autoregressive graph generative models through maximum likelihood estimation X Han, X Chen, FJR Ruiz, LP Liu Journal of Machine Learning Research 24 (97), 1-30, 2023 | 8 | 2023 |
| PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations H Gao, X Han, J Huang, JX Wang, LP Liu Learning on Graphs Conference (LoG2022), 2022 | 5 | 2022 |
| Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning L Liu, X Han, D Zhou, LP Liu TMLR, 2022 | 5 | 2022 |
| Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model L Sun, X Han, H Gao, JX Wang, L Liu Conference on Neural Information Processing Systems (Neurips 2023), 2023 | 4 | 2023 |
| Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation H Gao, X Han, X Fan, L Sun, LP Liu, L Duan, JX Wang Computer Methods in Applied Mechanics and Engineering 427, 117023, 2024 | 2 | 2024 |
| Training-free Multi-objective Diffusion Model for 3D Molecule Generation X Han, C Shan, Y Shen, C Xu, H Yang, X Li, D Li The Twelfth International Conference on Learning Representations, 2023 | 2 | 2023 |
Li-Ping LiuTufts University在 tufts.edu 的电子邮件经过验证
