A Tutorial on Parametric Variational Inference

2023-01-03 · Jens Sjölund

Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.

📄 PDF Abstract BibTeX arXiv:2301.01236

Code (0)

등록된 구현이 없습니다.

Tasks

Variational Inference

Methods 이 논문이 사용한 방법론

Variational Inference 설명 없음

Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning

2019-08-25 · Xudong Sun, Bernd Bischl

Aiming at a comprehensive and concise tutorial survey, recap of variational inference and reinforcement learning with Probabilistic Graphical Models are given with detailed derivations. Reviews and comparisons on recent …

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+2

A practical tutorial on Variational Bayes

2021-03-01 · Minh-Ngoc Tran, Trong-Nghia Nguyen, Viet-Hung Dao

This tutorial gives a quick introduction to Variational Bayes (VB), also called Variational Inference or Variational Approximation, from a practical point of view. The paper covers a range of commonly used VB methods and…

Bayesian Inference Variational Inference

Variational Inference and Deep Generative Models

2018-07-01 · ACL 2018 7 · Wilker Aziz, Philip Schulz

NLP has seen a surge in neural network models in recent years. These models provide state-of-the-art performance on many supervised tasks. Unsupervised and semi-supervised learning has only been addressed scarcely, howev…

Machine Translation Natural Language Inference Variational Inference

Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Function

2019-07-21 · Stephen Odaibo

In Bayesian machine learning, the posterior distribution is typically computationally intractable, hence variational inference is often required. In this approach, an evidence lower bound on the log likelihood of data is…