This document provides an overview of POMDP (Partially Observable Markov Decision Process) and its applications. It first defines the key concepts of POMDP such as states, actions, observations, and belief states. It then uses the classic Tiger problem as an example to illustrate these concepts. The document discusses different approaches to solve POMDP problems, including model-based methods that learn the environment model from data and model-free reinforcement learning methods. Finally, it provides examples of applying POMDP to games like ViZDoom and robot navigation problems.
This document provides an overview of POMDP (Partially Observable Markov Decision Process) and its applications. It first defines the key concepts of POMDP such as states, actions, observations, and belief states. It then uses the classic Tiger problem as an example to illustrate these concepts. The document discusses different approaches to solve POMDP problems, including model-based methods that learn the environment model from data and model-free reinforcement learning methods. Finally, it provides examples of applying POMDP to games like ViZDoom and robot navigation problems.
CVPR2016 Fitting Surface Models to Data 抜粋sumisumith
This document provides a summary of a CVPR 2016 tutorial on fitting surface models to data. The tutorial covered several applications of surface fitting including curve and surface fitting, parameter estimation, bundle adjustment, and more. It discussed fitting subdivision surfaces and polygon meshes to 2D and video data. Specific examples of fitting hand models to data for applications like hand tracking were presented. The tutorial aimed to teach attendees how to solve hard vision problems using tools that may seem inelegant but are smarter than they appear for fitting models to data.
This is the slide about comparing distributed GPU processing between some DeepLearning Flameworks on TensorFlow User Group #4.
The meetup was in Tokyo on 2017/04/19.
https://tfug-tokyo.connpass.com/event/54396/