37 citations · 122 across the 8 of their papers we have counts for
5 papers · 1 filter
Implicit Normalizing Flows
Cheng Lu, Jianfei Chen, Chongxuan Li +2
Normalizing flows define a probability distribution by an explicit invertible transformation . In this work, we present implicit…
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Jianfei Chen, Cheng Lu, Biqi Chenli +2
Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations. However, tractability imposes architec…
ZhuSuan: A Library for Bayesian Deep Learning
Jiaxin Shi, Jianfei Chen, Jun Zhu +4
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep…
Scalable Inference for Nested Chinese Restaurant Process Topic Models
Jianfei Chen, Jun Zhu, Jie Lu +1
Nested Chinese Restaurant Process (nCRP) topic models are powerful nonparametric Bayesian methods to extract a topic hierarchy from a given text corpus, where the hierarchical stru…
Scaling up Dynamic Topic Models
Arnab Bhadury, Jianfei Chen, Jun Zhu +1
Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods a…