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Tianbo Li
Tianbo Li
SEA AI Lab
在 sea.com 的电子邮件经过验证
标题
引用次数
引用次数
年份
Nonparametric generative modeling with conditional sliced-Wasserstein flows
C Du, T Li, T Pang, S Yan, M Lin
arXiv preprint arXiv:2305.02164, 2023
122023
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
82023
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
82020
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
62021
Transfer hawkes processes with content information
T Li, P Wei, Y Ke
2018 IEEE International Conference on Data Mining (ICDM), 1116-1121, 2018
62018
Thinning for accelerating the learning of point processes
T Li, Y Ke
Advances in Neural Information Processing Systems 32, 2019
52019
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
12023
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
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