Add to ORKG1. Variational inference and learning We derive the variational inference updates in this sec-tion. We use these updates as an E-step in a variational EM framework that is guaranteed to increase a lower bound on the data likelihood. The variational inference updates for our Q-distributions are given below. Q∗(smj) ∝∑ um
This paper introduces the $\textit{variational Rényi bound}$ (VR) that extends traditional variation...
<p>Stochastic variational inference finds good posterior approximations of probabilistic models with...
Variational inference has become a widely used method to approximate posteriors in complex latent va...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
We present a general method for deriving collapsed variational inference algo-rithms for probabilist...
We describe a variational approximation method for e cient inference in large-scale probabilistic mo...
Derivation of the variational expectation maximization (EM) inference algorithm. Derivation of the v...
We describe a variational approximation method for efficient inference in large-scale probabilistic ...
We present a general method for deriving collapsed variational inference algorithms for probabilisti...
In this extra material, we provide more details about the variational EM algorithm for multi-task an...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
This paper presents Variational Message Passing (VMP), a general purpose algorithm for applying vari...
Many methods for machine learning rely on approximate inference from intractable probability distrib...
<p>One of the core problems of modern statistics is to approximate difficult-to-compute probability ...
We present a novel method for approximate inference. Using some of the constructs from expectation p...
This paper introduces the $\textit{variational Rényi bound}$ (VR) that extends traditional variation...
<p>Stochastic variational inference finds good posterior approximations of probabilistic models with...
Variational inference has become a widely used method to approximate posteriors in complex latent va...
Variational inference is one of the tools that now lies at the heart of the modern data analysis lif...
We present a general method for deriving collapsed variational inference algo-rithms for probabilist...
We describe a variational approximation method for e cient inference in large-scale probabilistic mo...
Derivation of the variational expectation maximization (EM) inference algorithm. Derivation of the v...
We describe a variational approximation method for efficient inference in large-scale probabilistic ...
We present a general method for deriving collapsed variational inference algorithms for probabilisti...
In this extra material, we provide more details about the variational EM algorithm for multi-task an...
This tutorial describes the mean-field variational Bayesian approximation to inference in graphical ...
This paper presents Variational Message Passing (VMP), a general purpose algorithm for applying vari...
Many methods for machine learning rely on approximate inference from intractable probability distrib...
<p>One of the core problems of modern statistics is to approximate difficult-to-compute probability ...
We present a novel method for approximate inference. Using some of the constructs from expectation p...
This paper introduces the $\textit{variational Rényi bound}$ (VR) that extends traditional variation...
<p>Stochastic variational inference finds good posterior approximations of probabilistic models with...
Variational inference has become a widely used method to approximate posteriors in complex latent va...