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Bayesian Deep Learning and a Probabilistic Perspective of Generalization


Andrew Gordon Wilson: Our new paper "Bayesian Deep Learning and a Probabilistic Perspective of Generalization": Includes (1) benefits of BMA; (2) BMA <-> Deep Ensembles; (3) new methods; (4) BNN priors; (5) generalization in DL; (6) tempering in BDL. With @Pavel_Izmailov. 1/19

10 replies, 1184 likes

Andrew Gordon Wilson: Bayesian model averaging mitigates double descent! We have just posted this new result in section 7 of our paper on Bayesian deep learning with @Pavel_Izmailov: The result highlights the importance of *multi-modal* marginalization with Multi-SWAG. 1/3

2 replies, 427 likes

hardmaru: New paper from @andrewgwils and @Pavel_Izmailov adds to the recent discussion on Bayesian deep learning!

0 replies, 125 likes

Jasper: Another perspective in the debate on Bayesian deep learning. I love how this academic discussion is progressing, hopefully to the result of a better understanding and new methods!

0 replies, 69 likes

Miles Cranmer: Paper (@Pavel_Izmailov et al) + fit an entire Gaussian to the weight posterior mode, which gives you uncertainty info: + repeat for several modes (~epistemic uncertainty) with:

2 replies, 62 likes

no love deep learning: """ There is a tendency to classify work as Bayesian or not Bayesian, with very stringent criteria for what qualifies as Bayesian [...] We believe this mentality encourages tribalism, which is not conductive to the best research """ words of wisdom by @andrewgwils @Pavel_Izmailov

1 replies, 55 likes

Nathan Ratliff: One of the best papers I've read recently: Bayesian Deep Learning and a Probabilistic Perspective of Generalization Clear, elegant, and well-supported. We're starting to understand generalization in DL, and Bayesian model averaging can be practical.

0 replies, 37 likes

Adrian Raftery: "Deep ensembles = Bayesian model averaging". Connections between BMA, deep learning, and PAC-Bayes.

0 replies, 23 likes

Andrew Gordon Wilson: Translation equivariance has imbued CNNs with powerful generalization abilities. Our #NeurIPS2020 paper shows how to *learn* symmetries -- rotations, translations, scalings, shears -- from training data alone! w/ @g_benton_, @Pavel_Izmailov, @m_finzi. 1/9

1 replies, 12 likes

Andrew Gordon Wilson: @davidwhogg You may be interested in, where we show that Bayesian model averaging mitigates double descent (as predicted by Sec 1 & 3). We also provide an explanation for DD in

0 replies, 11 likes

Robert Peharz: A Sober Look at Bayesian Neural Networks πŸ‘‡

0 replies, 10 likes

Hector Yee: My favorite paper figure this week, model inductive bias in terms of support vs evidence. Totally going to cite it at a meeting next week

0 replies, 9 likes

Andrew Gordon Wilson: @HemilDesai10 Thanks Hemil. There aren’t video recordings of those lectures. But some of the material is in our recent paper:

1 replies, 8 likes

Statistics Papers: Bayesian Deep Learning and a Probabilistic Perspective of Generalization.

0 replies, 7 likes

Drew Dimmery: Is this the first time someone has chosen (2), @andrewgwils ? (context:

0 replies, 6 likes

HotComputerScience: Most popular computer science paper of the day: "Bayesian Deep Learning and a Probabilistic Perspective of Generalization"

0 replies, 6 likes

Quiche ~Lorraine~: Putting this on my list of things to read.

1 replies, 5 likes

Adam Cobb: Really enjoyed reading and thanks for giving hamiltorch a go!

0 replies, 3 likes

tj mahr πŸ•πŸ: great figure

0 replies, 2 likes


Found on Feb 21 2020 at

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