Andrew Gordon Wilson: Our new paper "Bayesian Deep Learning and a Probabilistic Perspective of Generalization": https://arxiv.org/abs/2002.08791. 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 https://t.co/OG3n2EJve3
10 replies, 1186 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: https://arxiv.org/abs/2002.08791. The result highlights the importance of *multi-modal* marginalization with Multi-SWAG. 1/3 https://t.co/ZbhxGdjW5I
3 replies, 424 likes
CEA: To better visualize observed data, we also continually update a curve-fitting exercise to summarize COVID-19's observed trajectory. Particularly with irregular data, curve fitting can improve data visualization. As shown, IHME's mortality curves have matched the data fairly well. https://t.co/NtJcOdA98R
862 replies, 190 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) https://arxiv.org/abs/1803.05407
+ fit an entire Gaussian to the weight posterior mode, which gives you uncertainty info: https://arxiv.org/abs/1902.02476
+ repeat for several modes (~epistemic uncertainty) with: https://arxiv.org/pdf/2002.08791.pdf
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
Adrian Raftery: "Deep ensembles = Bayesian model averaging". Connections between BMA, deep learning, and PAC-Bayes.
0 replies, 23 likes
Andrew Gordon Wilson: @davidwhogg You may be interested in https://arxiv.org/abs/2002.08791, where we show that Bayesian model averaging mitigates double descent (as predicted by Sec 1 & 3). We also provide an explanation for DD in https://arxiv.org/abs/2003.02139.
0 replies, 11 likes
Robert Peharz: A Sober Look at Bayesian Neural Networks 👇
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 https://arxiv.org/pdf/2002.08791.pdf https://t.co/AORTWZahjF
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: https://arxiv.org/abs/2002.08791
1 replies, 8 likes
Statistics Papers: Bayesian Deep Learning and a Probabilistic Perspective of Generalization. http://arxiv.org/abs/2002.08791
0 replies, 7 likes
HotComputerScience: Most popular computer science paper of the day:
"Bayesian Deep Learning and a Probabilistic Perspective of Generalization"
0 replies, 6 likes
Drew Dimmery: Is this the first time someone has chosen (2), @andrewgwils ?
(context: https://arxiv.org/pdf/2002.08791.pdf) https://t.co/TL1MNM4IE8
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 🍕🍍: great figure
0 replies, 2 likes
Found on Feb 21 2020 at https://arxiv.org/pdf/2002.08791.pdf