%0 Journal Article %1 kobyzev2019normalizing %A Kobyzev, Ivan %A Prince, Simon %A Brubaker, Marcus A. %D 2019 %K flows generative-models survey %T Normalizing Flows: An Introduction and Review of Current Methods %U http://arxiv.org/abs/1908.09257 %X Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the literature around the construction and use of Normalizing Flows for distribution learning. We aim to provide context and explanation of the models, review current state-of-the-art literature, and identify open questions and promising future directions.

@article{kobyzev2019normalizing, abstract = {Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the literature around the construction and use of Normalizing Flows for distribution learning. We aim to provide context and explanation of the models, review current state-of-the-art literature, and identify open questions and promising future directions.}, added-at = {2020-03-24T19:35:07.000+0100}, author = {Kobyzev, Ivan and Prince, Simon and Brubaker, Marcus A.}, biburl = {https://www.bibsonomy.org/bibtex/265f8c87bd9c87a70066ca571ecece734/kirk86}, description = {[1908.09257] Normalizing Flows: An Introduction and Review of Current Methods}, interhash = {a2246329fa804990c892b9439d1c22b1}, intrahash = {65f8c87bd9c87a70066ca571ecece734}, keywords = {flows generative-models survey}, note = {cite arxiv:1908.09257Comment: Updated version, currently under review in a journal}, timestamp = {2020-03-24T19:35:07.000+0100}, title = {Normalizing Flows: An Introduction and Review of Current Methods}, url = {http://arxiv.org/abs/1908.09257}, year = 2019 }