Chang Liu
Chang Liu
Microsoft Research Asia
Zweryfikowany adres z microsoft.com - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Message Passing Stein Variational Gradient Descent
J Zhuo, C Liu, J Shi, J Zhu, N Chen, B Zhang
International Conference on Machine Learning, 2018
492018
Riemannian Stein Variational Gradient Descent for Bayesian Inference
C Liu, J Zhu
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
442018
Understanding and Accelerating Particle-Based Variational Inference
C Liu, J Zhuo, P Cheng, R Zhang, J Zhu, L Carin
International Conference on Machine Learning, 4082--4092, 2019
39*2019
Invertible image rescaling
M Xiao, S Zheng, C Liu, Y Wang, D He, G Ke, J Bian, Z Lin, TY Liu
European Conference on Computer Vision, 126-144, 2020
372020
Stochastic Gradient Geodesic MCMC Methods
C Liu, J Zhu, Y Song
Advances in Neural Information Processing Systems, 3009-3017, 2016
272016
Variational Annealing of GANs: A Langevin Perspective
C Tao, S Dai, L Chen, K Bai, J Chen, C Liu, R Zhang, G Bobashev, ...
International Conference on Machine Learning, 6176-6185, 2019
102019
Modeling Lost Information in Lossy Image Compression
Y Wang, M Xiao, C Liu, S Zheng, TY Liu
arXiv preprint arXiv:2006.11999, 2020
82020
Generalizing to Unseen Domains: A Survey on Domain Generalization
J Wang, C Lan, C Liu, Y Ouyang, W Zeng, T Qin
arXiv preprint arXiv:2103.03097, 2021
62021
Learning Causal Semantic Representation for Out-of-Distribution Prediction
C Liu, X Sun, J Wang, H Tang, T Li, T Qin, W Chen, TY Liu
arXiv preprint arXiv:2011.01681, 2020
62020
Straight-Through Estimator as Projected Wasserstein Gradient Flow
P Cheng, C Liu, C Li, D Shen, R Henao, L Carin
NeurIPS 2018 Bayesian Deep Learning Workshop, 2018
62018
Latent Causal Invariant Model
X Sun, B Wu, C Liu, X Zheng, W Chen, T Qin, TY Liu
arXiv preprint arXiv:2011.02203, 2020
42020
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
MH Zhu, C Liu, J Zhu
International Conference on Machine Learning, 9500--9511, 2020
42020
Understanding MCMC Dynamics as Flows on the Wasserstein Space
C Liu, J Zhuo, J Zhu
International Conference on Machine Learning, 4093--4103, 2019
42019
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Driven Adaptive Prior
S Lee, H Kim, C Shin, X Tan, C Liu, Q Meng, T Qin, W Chen, S Yoon, ...
arXiv preprint arXiv:2106.06406, 2021
22021
Learning to Match Distributions for Domain Adaptation
C Yu, J Wang, C Liu, T Qin, R Xu, W Feng, Y Chen, TY Liu
arXiv preprint arXiv:2007.10791, 2020
12020
On the Generative Utility of Cyclic Conditionals
C Liu, H Tang, T Qin, J Wang, TY Liu
arXiv preprint arXiv:2106.15962, 2021
2021
Sampling with Mirrored Stein Operators
J Shi, C Liu, L Mackey
arXiv preprint arXiv:2106.12506, 2021
2021
Learning Invariant Representations across Domains and Tasks
J Wang, W Feng, C Liu, C Yu, M Du, R Xu, T Qin, TY Liu
arXiv preprint arXiv:2103.05114, 2021
2021
Sampling Methods on Manifolds and Their View from Probability Manifolds
C Liu
2019
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