Fan Bao (鲍凡)   Co-founder and CTO of ShengShu Email: fan.bao@shengshu.ai Github: baofff [Google Scholar]

Biography

I received my Ph.D. degree from TSAIL Group in the Department of Computer Science and Technology, Tsinghua University, advised by Prof. Jun Zhu and Prof. Bo Zhang. Before that, I received my B.S. degree from the Department of Computer Science and Technology, Tsinghua University in July, 2019.

My research interest includes machine learning and deep learning. Recently, I am interested in diffusion models and its applications, and I have led a series of works on diffusion models, including Analytic-DPM, U-ViT, UniDiffuser, and Vidu.

Publications

2024
• Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models
  Fan Bao, Chendong Xiang, Gang Yue, Guande He, Hongzhou Zhu, Kaiwen Zheng, Min Zhao, Shilong Liu, Yaole Wang, Jun Zhu
  arXiv preprint
  [arXiv]

2023
• Gaussian Mixture Solvers for Diffusion Models
  Hanzhong Allan Guo, Cheng Lu, Fan Bao, Tianyu Pang, Shuicheng Yan, Chao Du, Chongxuan Li Conference on Neural Information Processing Systems (NeurIPS), 2023
  [arXiv]
• Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
  Spotlight
  Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li, Jun Zhu Conference on Neural Information Processing Systems (NeurIPS), 2023
  [arXiv]
• ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
  Spotlight
  Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, Jun Zhu
  Conference on Neural Information Processing Systems (NeurIPS), 2023
  [arXiv] [code]
• One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
  Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu
  International Conference on Machine Learning (ICML), 2023
  [arXiv] [code]
• Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
  Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu
  International Conference on Machine Learning (ICML), 2023
  [arXiv]
• All are Worth Words: a ViT Backbone for Diffusion Models
  Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, Jun Zhu
  Conference on Computer Vision and Pattern Recognition (CVPR), 2023
  [arXiv] [code]
• Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models
  Yong Zhong, Hongtao Liu, Xiaodong Liu, Fan Bao, Weiran Shen, Chongxuan Li
  International Conference on Learning Representations (ICLR), 2023
  [arXiv]
• Equivariant Energy-Guided SDE for Inverse Molecular Design
  Fan Bao*, Min Zhao*, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu
  International Conference on Learning Representations (ICLR), 2023
  [arXiv] [code]

2022
• Why Are Conditional Generative Models Better Than Unconditional Ones?
  Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu
  Score-based Model workshop @ NeurIPS, 2022
  [arXiv]
• EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations
  Min Zhao, Fan Bao, Chongxuan Li, Jun Zhu
  Conference on Neural Information Processing Systems (NeurIPS), 2022
  [arXiv] [code]
• DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
  Oral (Accept rate~1.7%)
  Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu
  Conference on Neural Information Processing Systems (NeurIPS), 2022
  [arXiv] [code]
• Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
  Fan Bao, Chongxuan Li, Jiacheng Sun, Jun Zhu, Bo Zhang
  International Conference on Machine Learning (ICML), 2022
  [arXiv] [code]
• Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching
  Cheng Lu, Kaiwen Zheng, Fan Bao, Chongxuan Li, Jianfei Chen, Jun Zhu
  International Conference on Machine Learning (ICML), 2022
  
• Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
  Outstanding Paper Award (Top 7/3391)
  Fan Bao, Chongxuan Li, Jun Zhu, Bo Zhang
  International Conference on Learning Representations (ICLR), 2022
  [arXiv] [code]

2021
• Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
  Fan Bao*, Guoqiang Wu*, Chongxuan Li*, Jun Zhu, Bo Zhang
  Conference on Neural Information Processing Systems (NeurIPS), 2021
  [arXiv] [code]
• Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
  Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang
  International Conference on Machine Learning (ICML), 2021
  [arXiv] [code]

2020
• Bi-level Score Matching for Learning Energy-based Latent Variable Models
  Fan Bao*, Chongxuan Li*, Kun Xu, Hang Su, Jun Zhu, Bo Zhang
  Conference on Neural Information Processing Systems (NeurIPS), 2020
  [arXiv] [code]

Honors & Awards

Huawei Scholarship, 2023

LongHu Scholarship · Excellence Award, 2023

ZhongShimo Scholarship, 2022

China National Scholarship, 2022

ByteDance Scholarship, 2022.10

Huawei Innovation Pioneer First Prize, 2022

International Conference on Learning Representations (ICLR) Outstanding Paper Awards, 2022

Invited Talks

Large Multi-Modal Generative Models, CCAI 2023, 2023.7
Diffusion Probabilistic Models: Foundations, Fast Inference and Controllable Generation, BAAI Large Model Innovation Forum 2022, 2022.12
Diffusion Probabilistic Models: Theory and Applications, Shanghai Artificial Intelligence Laboratory Xingqi Talk, 2022.11
Diffusion Probabilistic Models: Theory and Applications, Jiqi Zhixin, 2022.11
Applications of Diffusion Models, ByteDance, 2022.5 [slide]