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Normalizing Flows for Probabilistic Modeling and Inference


Danilo J. Rezende: Looking for something to read in your flight to #NeurIPS2019? Read about Normalizing Flows from our extensive review paper (also with new insights on how to think about and derive new flows) with @gpapamak @eric_nalisnick @DeepSpiker @balajiln @shakir_za

11 replies, 651 likes

George Papamakarios: Check out our extensive review paper on normalizing flows! This paper is the product of years of thinking about flows: it contains everything we know about them, and many new insights. With @eric_nalisnick, @DeepSpiker, @shakir_za, @balajiln. Thread 👇

4 replies, 645 likes

Andrej Karpathy: This is very well done and readable, thank you so much for putting it together! (was starting to be hard to keep track of the individual papers and how they relate) Normalizing Flows are very powerful and should become a part of any deep learning researcher/practitioner's toolbox

5 replies, 479 likes

David Duvenaud: This review on normalizing flows is excellent. It's full of clear writing, precise claims, and useful connections.

0 replies, 146 likes

Eric Nalisnick: Indeed, what better way to spend your flight than immersed in 60 pages of all things flow. Happy to hear any feedback (oversights, clarity issues, etc).

3 replies, 64 likes

Adji Bousso Dieng: @yoavgo Some of my favorite review papers (recent): MC gradient estimation in ML by @shakir_za et al. Normalizing flows by @gpapamak et al. Variational inference (pre-amortization) by Blei et al. (@blei_lab)

2 replies, 54 likes

Statistics Papers: Normalizing Flows for Probabilistic Modeling and Inference.

0 replies, 53 likes

jörn jacobsen: Fantastic summary of the state of the art and open problems in normalizing flows!

0 replies, 30 likes

Shasha Feng: What's popular with ML in MD now?🤔 Normalizing flows! This review of flow method by Google last Dec: And 2 arkiv papers this month:

0 replies, 27 likes

Ari Seff: And for an extensive review, check out the just-released "Normalizing Flows for Probabilistic Modeling and Inference" ( from @gpapamak @eric_nalisnick @DeepSpiker @balajiln @shakir_za

0 replies, 13 likes

Adam Kosiorek: The most comprehensive overview of normalizing flows to date!

0 replies, 12 likes

no love deep learning ✈ NeuHYPE19: we'll have open-mic sessions to ignite discussions, as well as invited spotlights! First one will be @eric_nalisnick (@CambridgeMLG & @DeepMindAI ) talking about prospects and challenges of #tractable Inference with #Flows check his survey with @gpapamak

1 replies, 9 likes

no love deep learning: @martin_trapp Even without using mixtures one might easily fit those 2D densities with more expressive single #flows and #autoeegressive ones They *are* also tractable for complete evidence, but not marginals Cf. cool survey by @eric_nalisnick @gpapamak

1 replies, 8 likes

Roman Shapovalov: This tutorial is gold. Very accessible, comprehensive, and up-to-date. Thanks @gpapamak et al.!

0 replies, 5 likes

Bo Wang: This is very useful resource for anyone who is interested in deep learning research!

0 replies, 5 likes

Dennis Prangle: One for the ever-lengthening must-read list

1 replies, 4 likes

Hyunjik Kim: Review paper on flows from the experts!

0 replies, 4 likes

Gabriel Bernier-Colborne: Great survey paper on normalizing flows:

0 replies, 1 likes

Aravind Srinivas: @CShorten30 @EdwardDixon3 @lilianweng Check out by DeepMind; and @pabbeel 's lecture in Berkeley Unsupervised Learning Class -

0 replies, 1 likes

KUNDAN KUMAR: Read it. Amazing!!

0 replies, 1 likes


Found on Dec 06 2019 at

PDF content of a computer science paper: Normalizing Flows for Probabilistic Modeling and Inference