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 | 204 | 2020 |
Generative latent flow Z Xiao, Q Yan, Y Amit arXiv preprint arXiv:1905.10485, 2019 | 37 | 2019 |
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 | 23 | 2021 |
Generative latent flow: A framework for non-adversarial image generation Z Xiao, Q Yan, Y Chen, Y Amit arXiv preprint arXiv:1905.10485, 2019 | 13 | 2019 |
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 | 9 | 2023 |
A method to model conditional distributions with normalizing flows Z Xiao, Q Yan, Y Amit arXiv preprint arXiv:1911.02052, 2019 | 9 | 2019 |
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 | 7 | 2020 |
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 | 3 | 2021 |
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 | 1 | 2024 |
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 | | |