Online Bayesian Deep Learning in Production at Tencent:
— Ferenc Huszár🇪🇺 (@fhuszar) November 15, 2018
my post on Tencent's scaleable click-prediction system using probabilistic backpropagation:https://t.co/o4koEQjt8D
Online Bayesian Deep Learning in Production at Tencent:
— Ferenc Huszár🇪🇺 (@fhuszar) November 15, 2018
my post on Tencent's scaleable click-prediction system using probabilistic backpropagation:https://t.co/o4koEQjt8D
The paper has been a long time coming. To Edward users: apologies for the delays. Hopefully the implementation is as polished as can be.
— Dustin Tran (@dustinvtran) November 7, 2018
This is the other reason I worry about an MCMC-first Bayesian resurgence: it's unclear to me that the benefits relative to "just optimize the log likelihood and use Fisher information" justify the >100x performance costs. https://t.co/ksSdGX39dZ
— John Myles White (@johnmyleswhite) October 24, 2018
ICYMI: ⭐️, thorough code-through by @krstoffr
— Mara Averick (@dataandme) October 17, 2018
"Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Pt 1: lognormal models" https://t.co/5xUpiSrIlO #rstats #statistics #dataviz pic.twitter.com/bVxuuXQ3Yj
👍 Classic intro / how-to:
— Mara Averick (@dataandme) October 15, 2018
"Easy Bayesian Bootstrap in R" by @rabaathhttps://t.co/cEaVxaJeHV #rstats #bootstrap pic.twitter.com/f6tFfHNU55
Bayesian CNNs with many channels are Gaussian processes! One can compute test set predictions that would have resulted from fully Bayesian training of a CNN, but without ever instantiating a CNN, and instead by evaluating the corresponding GP. https://t.co/VOdpOKRE5g pic.twitter.com/5Rr9jiLJWW
— Jascha (@jaschasd) October 12, 2018
ArviZ: Exploratory analysis of Bayesian models
— ML Review (@ml_review) October 7, 2018
By @aloctavodia @colindcarroll
Includes functions for posterior analysis, model checking, comparison and diagnostics.https://t.co/i4zt7WC5h9 pic.twitter.com/XCUCWKNBrv
ICYMI, 📃 rstanarm, RStan…
— Mara Averick (@dataandme) October 5, 2018
"bayesplot: cheatsheets for the Stan ecosystem" ✏️ Edward Roualdes
https://t.co/LTFm9wMReK #rstan #rstats #dataviz pic.twitter.com/mvz1N2QL8u
"An Introduction to Probabilistic Programming" 218 pp 📖
— ML Review (@ml_review) October 1, 2018
Book Draft by @jwvdm @hyang144 @frankdonaldwood https://t.co/WYJu7NwGCO pic.twitter.com/AcX67FUut4
Bayesian Optimization at Facebook: https://t.co/XBnYzMthIW
— John Myles White (@johnmyleswhite) September 17, 2018
ICYMI, peep pkg @bluecology's pkg overview…
— Mara Averick (@dataandme) September 16, 2018
🚗 "Choosing R packages for mixed effects modelling based on the car you drive"https://t.co/R0U5V9vx41 #rstats pic.twitter.com/qGFkgTzFm4
ICYMI: rstanarm, RStan and more…
— Mara Averick (@dataandme) September 5, 2018
📃 "bayesplot: cheatsheets for the Stan ecosystem" ✏️ Edward Roualdes
https://t.co/LTFm9wMReK #rstan #rstats #dataviz pic.twitter.com/SUQzmy5K9S