Nonparametric generative modeling with conditional sliced-Wasserstein flows C Du, T Li, T Pang, S Yan, M Lin arXiv preprint arXiv:2305.02164, 2023 | 12 | 2023 |
D4FT: A deep learning approach to Kohn-Sham density functional theory T Li, M Lin, Z Hu, K Zheng, G Vignale, K Kawaguchi, AH Neto, ... arXiv preprint arXiv:2303.00399, 2023 | 8 | 2023 |
Tweedie-Hawkes Processes: Interpreting the Phenomena of Outbreaks T Li, Y Ke Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4699-4706, 2020 | 8 | 2020 |
Mitigating performance saturation in neural marked point processes: Architectures and loss functions T Li, T Luo, Y Ke, SJ Pan Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 6 | 2021 |
Transfer hawkes processes with content information T Li, P Wei, Y Ke 2018 IEEE International Conference on Data Mining (ICDM), 1116-1121, 2018 | 6 | 2018 |
Thinning for accelerating the learning of point processes T Li, Y Ke Advances in Neural Information Processing Systems 32, 2019 | 5 | 2019 |
Neural integral functionals Z Hu, T Li, Z Shi, K Zheng, G Vignale, K Kawaguchi, YAN Shuicheng, ... ICLR 2023 Workshop on Physics for Machine Learning, 2023 | 1 | 2023 |
Amortized Eigendecomposition for Neural Networks T Li, Z Shi, J Zhao, M Lin The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | | 2024 |
Diagonalization without Diagonalization: A Direct Optimization Approach for Solid-State Density Functional Theory T Li, M Lin, S Dale, Z Shi, AH Neto, KS Novoselov, G Vignale arXiv preprint arXiv:2411.05033, 2024 | | 2024 |
Differentiable Optimization in Plane-Wave Density Functional Theory for Solid States T Li, SG Dale, Z Shi, J Li, G Vignale, AHC Neto, KS Novoselov, M Lin | | 2023 |
Jrystal: A JAX-based Differentiable Density Functional Theory Framework for Materials T Li, Z Shi, SG Dale, G Vignale, M Lin | | |
Generalization in Neural Operator: Irregular Domains, Orthogonal Basis, and Super-Resolution Z Hu, Z Hao, T Li, Z Shi, K Kawaguchi, M Lin | | |