关注
Qing Yan
Qing Yan
Research Scientist, Bytedance Inc
在 bytedance.com 的电子邮件经过验证
标题
引用次数
引用次数
年份
Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Z Xiao, Q Yan, Y Amit
Advances in neural information processing systems 33, 20685-20696, 2020
2042020
Generative latent flow
Z Xiao, Q Yan, Y Amit
arXiv preprint arXiv:1905.10485, 2019
372019
Do we really need to learn representations from in-domain data for outlier detection?
Z Xiao, Q Yan, Y Amit
arXiv preprint arXiv:2105.09270, 2021
232021
Generative latent flow: A framework for non-adversarial image generation
Z Xiao, Q Yan, Y Chen, Y Amit
arXiv preprint arXiv:1905.10485, 2019
132019
MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion
D Chang, Y Shi, Q Gao, H Xu, J Fu, G Song, Q Yan, Y Zhu, X Yang, ...
Forty-first International Conference on Machine Learning, 2023
92023
A method to model conditional distributions with normalizing flows
Z Xiao, Q Yan, Y Amit
arXiv preprint arXiv:1911.02052, 2019
92019
Exponential tilting of generative models: Improving sample quality by training and sampling from latent energy
Z Xiao, Q Yan, Y Amit
arXiv preprint arXiv:2006.08100, 2020
72020
Ebms trained with maximum likelihood are generator models trained with a self-adverserial loss
Z Xiao, Q Yan, Y Amit
arXiv preprint arXiv:2102.11757, 2021
32021
MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
K Song, Y Zhu, B Liu, Q Yan, A Elgammal, X Yang
arXiv preprint arXiv:2404.05674, 2024
12024
System and a method for training a neural network having autoencoder architecture to recover missing data
E Laftchiev, Q Yan, D Nikovski
US Patent 11,698,946, 2023
2023
Deep Generative Models: Design, Improvements and Applications
Q Yan
The University of Chicago, 2022
2022
The Missing Input Problem
E Laftchiev, Q Yan, D Nikovski
2020 IEEE International Conference on Big Data (Big Data), 1565-1573, 2020
2020
Improving Sample Quality by Training and Sampling from Latent Energy
Z Xiao, Q Yan, Y Amit
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