Qinsheng Zhang

According to our database, Qinsheng Zhang authored at least 22 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Learning, sampling and inference with stochastic differential equations.

Qinsheng Zhang

PhD thesis, 2024

DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models.

Muyang Li, Tianle Cai, Jiaxin Cao, Qinsheng Zhang, Han Cai, Junjie Bai, Yangqing Jia, Ming-Yu Liu, Kai Li, Song Han

CoRR, 2024

Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion.

Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang, Chandramouli Shama Sastry, Siddharth Gururani, Sageev Oore, Yisong Yue

CoRR, 2024

2023

An Optimal Control Approach to Particle Filtering on Lie Groups.

Bo Yuan, Qinsheng Zhang, Yongxin Chen

IEEE Control. Syst. Lett., 2023

Improved Order Analysis and Design of Exponential Integrator for Diffusion Models Sampling.

Qinsheng Zhang, Jiaming Song, Yongxin Chen

CoRR, 2023

An optimal control approach to particle filtering.

Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen

Autom., 2023

Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation.

Jiaming Song, Qinsheng Zhang, Hongxu Yin, Morteza Mardani, Ming-Yu Liu, Jan Kautz, Yongxin Chen, Arash Vahdat

Proceedings of the International Conference on Machine Learning, 2023

gDDIM: Generalized denoising diffusion implicit models.

Qinsheng Zhang, Molei Tao, Yongxin Chen

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Fast Sampling of Diffusion Models with Exponential Integrator.

Qinsheng Zhang, Yongxin Chen

Proceedings of the Eleventh International Conference on Learning Representations, 2023

DiffCollage: Parallel Generation of Large Content with Diffusion Models.

Qinsheng Zhang, Jiaming Song, Xun Huang, Yongxin Chen, Ming-Yu Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Inference With Aggregate Data in Probabilistic Graphical Models: An Optimal Transport Approach.

Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson, Yongxin Chen

IEEE Trans. Autom. Control., 2022

Inference of Aggregate Hidden Markov Models With Continuous Observations.

Qinsheng Zhang, Rahul Singh, Yongxin Chen

IEEE Control. Syst. Lett., 2022

Learning hidden Markov models from aggregate observations.

Rahul Singh, Qinsheng Zhang, Yongxin Chen

Autom., 2022

Variational Wasserstein gradient flow.

Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen

Proceedings of the International Conference on Machine Learning, 2022

Path Integral Sampler: A Stochastic Control Approach For Sampling.

Qinsheng Zhang, Yongxin Chen

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Multi-Marginal Optimal Transport and Probabilistic Graphical Models.

Isabel Haasler, Rahul Singh, Qinsheng Zhang, Johan Karlsson, Yongxin Chen

IEEE Trans. Inf. Theory, 2021

Incremental Inference of Collective Graphical Models.

Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson, Yongxin Chen

IEEE Control. Syst. Lett., 2021

Diffusion Normalizing Flow.

Qinsheng Zhang, Yongxin Chen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Filtering for Aggregate Hidden Markov Models with Continuous Observations.

Qinsheng Zhang, Rahul Singh, Yongxin Chen

CoRR, 2020

Inference with Aggregate Data: An Optimal Transport Approach.

Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson, Yongxin Chen

CoRR, 2020

Improving Robustness via Risk Averse Distributional Reinforcement Learning.

Rahul Singh, Qinsheng Zhang, Yongxin Chen

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

2014

The Effect of Trust on Customers' Online Repurchase Intention in Consumer-to-Consumer Electronic Commerce.

Lin Jia, Casey G. Cegielski, Qinsheng Zhang

J. Organ. End User Comput., 2014