Variational inference is one of the tools that now lies at the heart of the modern data analysis lifecycle. Variational inference is the term used to encompass approximation techniques for the solution of intractable integrals and complex distributions and operates by transforming the hard problem of integration into one of optimisation. As a result, using variational inference we are now able to derive algorithms that allow us to apply increasingly complex probabilistic models to ever larger data sets on ever more powerful computing resources. This tutorial is meant as a broad introduction to modern approaches for approximate, large-scale inference and reasoning in probabilistic models. It is designed to be of interest to both new and expe...
Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines...
The variational approach to Bayesian inference enables simultaneous estimation of model parameters a...
Variational methods, which have become popular in the neural computing/machine learning literature, ...
We describe a variational approximation method for efficient inference in large-scale probabilistic ...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
<p>One of the core problems of modern statistics is to approximate difficult-to-compute probability ...
We describe a variational approximation method for e cient inference in large-scale probabilistic mo...
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variation...
Many machine learning problems deal with the estimation of conditional probabilities $p(y \mid x)$ f...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
Probabilistic inference is at the core of many recent advances in machine learning. Unfortunately, ...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions...
Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines...
The variational approach to Bayesian inference enables simultaneous estimation of model parameters a...
Variational methods, which have become popular in the neural computing/machine learning literature, ...
We describe a variational approximation method for efficient inference in large-scale probabilistic ...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
<p>One of the core problems of modern statistics is to approximate difficult-to-compute probability ...
We describe a variational approximation method for e cient inference in large-scale probabilistic mo...
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variation...
Many machine learning problems deal with the estimation of conditional probabilities $p(y \mid x)$ f...
The central objective of this thesis is to develop new algorithms for inference in probabilistic gra...
The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coh...
Probabilistic inference is at the core of many recent advances in machine learning. Unfortunately, ...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions...
Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines...
The variational approach to Bayesian inference enables simultaneous estimation of model parameters a...
Variational methods, which have become popular in the neural computing/machine learning literature, ...