Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows

2 code implementations • 3 May 2023 • Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan, Min Lin

Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric generative modeling but has not been widely adopted due to its suboptimal generative quality and lack of conditional modeling capabilities.

D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory

no code implementations • 1 Mar 2023 • Tianbo Li, Min Lin, Zheyuan Hu, Kunhao Zheng, Giovanni Vignale, Kenji Kawaguchi, A. H. Castro Neto, Kostya S. Novoselov, Shuicheng Yan

Kohn-Sham Density Functional Theory (KS-DFT) has been traditionally solved by the Self-Consistent Field (SCF) method.

Numerical Integration Total Energy

Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions

1 code implementation • 7 Jul 2021 • Tianbo Li, Tianze Luo, Yiping Ke, Sinno Jialin Pan

Neural marked point processes possess good interpretability of probabilistic models as well as the representational power of neural networks.

Model Selection Point Processes

Thinning for Accelerating the Learning of Point Processes

no code implementations • NeurIPS 2019 • Tianbo Li, Yiping Ke

Experimental results on synthetic and real-world datasets validate the effectiveness of thinning in the tasks of parameter and gradient estimation, as well as stochastic optimization.

Point Processes Stochastic Optimization