0


主动学习的无监督自举以解决实体问题

Unsupervised Bootstrapping of Active Learning for Entity Resolution
课程网址: http://videolectures.net/eswc2020_primpeli_paper_80/  
主讲教师: Anna Primpeli
开课单位: 曼海姆大学
开课时间: 2020-05-25
课程语种: 英语
中文简介:
在本教程中,我将讨论如何将强化学习(RL)与深度学习(DL)相结合。有几种方法可以将DL和RL结合在一起,包括基于价值,基于策略和基于模型的规划方法。这些方法中有几种存在众所周知的分歧问题,我将介绍解决这些不稳定性的简单方法。演讲将包括一个关于Atari 2600领域最近成功案例的案例研究,在该领域中,一个代理商可以直接从原始像素输入中学习直接玩许多不同的游戏。
课程简介: In this tutorial I will discuss how reinforcement learning (RL) can be combined with deep learning (DL). There are several ways to combine DL and RL together, including value-based, policy-based, and model-based approaches with planning. Several of these approaches have well-known divergence issues, and I will present simple methods for addressing these instabilities. The talk will include a case study of recent successes in the Atari 2600 domain, where a single agent can learn to play many different games directly from raw pixel input.
关 键 词: 主动学习; 深度学习; Atari
课程来源: 视频讲座网
数据采集: 2020-08-03:yumf
最后编审: 2020-08-03:yumf
阅读次数: 48