资源分类
》 资源 》论文》Efficient Structured Prediction with Latent Variables for General Graphical Models

Efficient Structured Prediction with Latent Variables for General Graphical Models

2020-02-28 | |  66 |   56 |   0

Abstract

In this paper we propose a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. We describe a local entropy approximation for this general formulation using duality, and derive an efficient message passing algorithm that is guaranteed to converge. We demonstrate its effectiveness in the tasks of image segmentation as well as 3D indoor scene understanding from single images, showing that our approach is superior to latent structured support vector machines and hidden conditional random fields.

上一篇：Parallelizing Exploration–Exploitation Tradeoffs with Gaussian Process Bandit Optimization

下一篇：Online Structured Prediction via Coactive Learning

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...