244 citations · 352 across the 7 of their papers we have counts for
13 papers
VIB is Half Bayes
Alexander A Alemi, Warren R Morningstar, Ben Poole +2
In discriminative settings such as regression and classification there are two random variables at play, the inputs X and the targets Y. Here, we demonstrate that the Variational I…
Density of States Estimation for Out-of-Distribution Detection
Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher +3
Perhaps surprisingly, recent studies have shown probabilistic model likelihoods have poor specificity for out-of-distribution (OOD) detection and often assign higher likelihoods to…
Automatic Differentiation Variational Inference with Mixtures
Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham +2
Automatic Differentiation Variational Inference (ADVI) is a useful tool for efficiently learning probabilistic models in machine learning. Generally approximate posteriors learned…
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
Junpeng Lao, Christopher Suter, Ian Langmore +7
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century. Its guarantees of asymptotic convergence, stability, and estimator-v…
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling +7
Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods…
Joint Distributions for TensorFlow Probability
Dan Piponi, Dave Moore, Joshua V. Dillon
A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms. We describe JointDistr…