Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC
By Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu. 2020
16 citations recorded by the source corpus.
Subfields: Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Probabilistic & Bayesian Methods
- Jan Kautz
- Karsten Kreis
- Arash Vahdat
- Zhisheng Xiao
- Yang Long
- Sam Bond-Taylor
- Adam Leach
- Chris G. Willcocks
Citations in the corpus
- VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models (2020, methodology): “Xiao et al. (2020); Nijkamp et al. (2020) use normalizing flows that transport complex data to latent variables to facilitate MCMC sampling (Hoffman et al., 2019), and thus, their methods can be viewed as EBMs with flow component.”
- Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models (2021, methodology): “This approach can be used to train a correction via exponential tilting [164], but can also be used to directly train an EBM and normalizing flow [54].”