
Bayesian Layers: A Module for Neural Network Uncertainty
We describe Bayesian Layers, a module designed for fast experimentation ...
12/10/2018 ∙ by Dustin Tran, et al. ∙ 14 ∙ shareread it

Measuring Calibration in Deep Learning
The reliability of a machine learning model's confidence in its predicti...
04/02/2019 ∙ by Jeremy Nixon, et al. ∙ 10 ∙ shareread it

Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
Obtaining reliable uncertainty estimates of neural network predictions i...
07/24/2018 ∙ by Danijar Hafner, et al. ∙ 8 ∙ shareread it

MeshTensorFlow: Deep Learning for Supercomputers
Batchsplitting (dataparallelism) is the dominant distributed Deep Neur...
11/05/2018 ∙ by Noam Shazeer, et al. ∙ 8 ∙ shareread it

Simple, Distributed, and Accelerated Probabilistic Programming
We describe a simple, lowlevel approach for embedding probabilistic pro...
11/05/2018 ∙ by Dustin Tran, et al. ∙ 6 ∙ shareread it

Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling hig...
05/24/2019 ∙ by Dustin Tran, et al. ∙ 6 ∙ shareread it

Autoconj: Recognizing and Exploiting Conjugacy Without a DomainSpecific Language
Deriving conditional and marginal distributions using conjugacy relation...
11/29/2018 ∙ by Matthew D. Hoffman, et al. ∙ 6 ∙ shareread it

Analyzing the Role of Model Uncertainty for Electronic Health Records
In medicine, both ethical and monetary costs of incorrect predictions ca...
06/10/2019 ∙ by Michael W. Dusenberry, et al. ∙ 3 ∙ shareread it

Hierarchical Implicit Models and LikelihoodFree Variational Inference
Implicit probabilistic models are a flexible class of models defined by ...
02/28/2017 ∙ by Dustin Tran, et al. ∙ 0 ∙ shareread it

Deep Probabilistic Programming
We propose Edward, a Turingcomplete probabilistic programming language....
01/13/2017 ∙ by Dustin Tran, et al. ∙ 0 ∙ shareread it

Variational Inference via χUpper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
11/01/2016 ∙ by Adji B. Dieng, et al. ∙ 0 ∙ shareread it

Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical in...
10/31/2016 ∙ by Dustin Tran, et al. ∙ 0 ∙ shareread it

Operator Variational Inference
Variational inference is an umbrella term for algorithms which cast Baye...
10/27/2016 ∙ by Rajesh Ranganath, et al. ∙ 0 ∙ shareread it

Automatic Differentiation Variational Inference
Probabilistic modeling is iterative. A scientist posits a simple model, ...
03/02/2016 ∙ by Alp Kucukelbir, et al. ∙ 0 ∙ shareread it

The Variational Gaussian Process
Variational inference is a powerful tool for approximate inference, and ...
11/20/2015 ∙ by Dustin Tran, et al. ∙ 0 ∙ shareread it

Hierarchical Variational Models
Black box variational inference allows researchers to easily prototype a...
11/07/2015 ∙ by Rajesh Ranganath, et al. ∙ 0 ∙ shareread it

Stochastic gradient descent methods for estimation with large data sets
We develop methods for parameter estimation in settings with largescale...
09/22/2015 ∙ by Dustin Tran, et al. ∙ 0 ∙ shareread it

Copula variational inference
We develop a general variational inference method that preserves depende...
06/10/2015 ∙ by Dustin Tran, et al. ∙ 0 ∙ shareread it

Towards stability and optimality in stochastic gradient descent
Iterative procedures for parameter estimation based on stochastic gradie...
05/10/2015 ∙ by Panos Toulis, et al. ∙ 0 ∙ shareread it

Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
A common approach for Bayesian computation with big data is to partition...
12/16/2014 ∙ by Andrew Gelman, et al. ∙ 0 ∙ shareread it

Convex Techniques for Model Selection
We develop a robust convex algorithm to select the regularization parame...
11/27/2014 ∙ by Dustin Tran, et al. ∙ 0 ∙ shareread it

Implicit Causal Models for Genomewide Association Studies
Progress in probabilistic generative models has accelerated, developing ...
10/30/2017 ∙ by Dustin Tran, et al. ∙ 0 ∙ shareread it

Flipout: Efficient PseudoIndependent Weight Perturbations on MiniBatches
Stochastic neural net weights are used in a variety of contexts, includi...
03/12/2018 ∙ by Yeming Wen, et al. ∙ 0 ∙ shareread it

TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability ...
11/28/2017 ∙ by Joshua V. Dillon, et al. ∙ 0 ∙ shareread it

NeuTralizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Hamiltonian Monte Carlo is a powerful algorithm for sampling from diffic...
03/09/2019 ∙ by Matthew Hoffman, et al. ∙ 0 ∙ shareread it