《Learning Structured Representation for Text Classification via Reinforcement Learning》论文翻译.md

摘要表征学习是自然语言处理中的一个基本问题.本文研究了如何学习文本分类的结构化表示.与大多数既不使用结构又依赖于预先指定结构的现有表示模型不同,我们提出了一种强化学习(RL)方法,通过自动覆盖优化结构来学习句子表示.我们演示了构建结构化表示的两种尝试:信息提取 LSTM(ID-LSTM)和层次结构LSTM(HS-LSTM).id-lstm只选择与任务相关的重要单词,hs-lstm发现句子中的短语结构.两个表示模型中的结构发现被表述为一个连续的决策问题:结构发现的当前决策影响后续决策,可以通过策…

Learning Structured Representation for Text Classification via Reinforcement Learning 学习笔记

Representation learning : 表征学习,端到端的学习 pre-specified 预先指定的 demonstrate 论证;证明,证实;显示,展示;演示,说明 attempt vt.尝试;试图 n. 进攻;尝试,冲击 distilled adj.由蒸馏得来的 v.蒸馏( distil的过去式和过去分词 );从…提取精华 relevant adj.有关的,中肯的;相关联的;确切的;有重大意义[作用]的,实质性的 phrase structures 短语结构 for…

论文笔记：Learning how to Active Learn: A Deep Reinforcement Learning Approach

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论文列表——text classification

https://blog.csdn.net/BitCs_zt/article/details/82938086 列出自己阅读的text classification论文的列表,以后有时间再整理相应的笔记.阅读价值评分纯粹是基于自己对于文章的理解,标准包括:动机.方法.数据集质量.实验安排.相关工作等,满分为5.列表如下: 名称所属会议类型时间阅读价值Recurrent Convolutional Neural Networks for Text Classification AAAI l…

(zhuan) Deep Reinforcement Learning Papers

Deep Reinforcement Learning Papers A list of recent papers regarding deep reinforcement learning. The papers are organized based on manually-defined bookmarks. They are sorted by time to see the recent papers first. Any suggestions and pull requests…

论文笔记之：Active Object Localization with Deep Reinforcement Learning

Active Object Localization with Deep Reinforcement Learning ICCV 2015 最近Deep Reinforcement Learning算是火了一把,在Google Deep Mind的主页上,更是许多关于此的paper,基本都发在ICML,AAAI,IJCAI等各种人工智能,机器学习的牛会顶刊,甚至是Nature,可以参考其官方publication page: https://www.deepmind.com/publicatio…

[DQN] What is Deep Reinforcement Learning

已经成为DL中专门的一派,高大上的样子 Intro: MIT 6.S191 Lecture 6: Deep Reinforcement Learning Course: CS 294: Deep Reinforcement Learning Jan 18: Introduction and course overview (Levine, Finn, Schulman) Slides: Levine Slides: Finn Slides: Schulman Video Why deep rei…

Learning Roadmap of Deep Reinforcement Learning

1. 知乎上关于DQN入门的系列文章 1.1 DQN 从入门到放弃 DQN 从入门到放弃1 DQN与增强学习 DQN 从入门到放弃2 增强学习与MDP DQN 从入门到放弃3 价值函数与Bellman方程 DQN 从入门到放弃4 动态规划与Q-Learning DQN从入门到放弃5 深度解读DQN算法 DQN从入门到放弃6 DQN的各种改进 DQN从入门到放弃7 连续控制DQN算法-NAF 12/29/2016 看完1和2: 1.2 Deep Reinforcement Learning 深度增…

(转) Deep Learning in a Nutshell: Reinforcement Learning

Deep Learning in a Nutshell: Reinforcement Learning Share: Posted on September 8, 2016by Tim Dettmers No CommentsTagged Deep Learning, Deep Neural Networks, Machine Learning,Reinforcement Learning This post is Part 4 of the Deep Learning in a Nutsh…

Awesome Reinforcement Learning

Awesome Reinforcement Learning A curated list of resources dedicated to reinforcement learning. We have pages for other topics: awesome-rnn, awesome-deep-vision, awesome-random-forest Maintainers: Hyunsoo Kim, Jiwon Kim We are looking for more contri…

论文笔记之：Asynchronous Methods for Deep Reinforcement Learning

Asynchronous Methods for Deep Reinforcement Learning ICML 2016 深度强化学习最近被人发现貌似不太稳定,有人提出很多改善的方法,这些方法有很多共同的 idea:一个 online 的 agent 碰到的观察到的数据序列是非静态的,然后就是,online的 RL 更新是强烈相关的.通过将 agent 的数据存储在一个 experience replay 单元中,数据可以从不同的时间步骤上,批处理或者随机采样.这种方法可以降低 non-st…

18 Issues in Current Deep Reinforcement Learning from ZhiHu

深度强化学习的18个关键问题 from: https://zhuanlan.zhihu.com/p/32153603 85 人赞了该文章深度强化学习的问题在哪里?未来怎么走?哪些方面可以突破? 这两天我阅读了两篇篇猛文A Brief Survey of Deep Reinforcement Learning 和 Deep Reinforcement Learning: An Overview ,作者排山倒海的引用了200多篇文献,阐述强化学习未来的方向.原文归纳出深度强化学习中的常见科学问题,…

(zhuan) Paper Collection of Multi-Agent Reinforcement Learning (MARL)

this blog from: https://github.com/LantaoYu/MARL-Papers Paper Collection of Multi-Agent Reinforcement Learning (MARL) This is a collection of research and review papers of multi-agent reinforcement learning (MARL). The sharing principle of these refe…

[转]Introduction to Learning to Trade with Reinforcement Learning

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Introduction to Learning to Trade with Reinforcement Learning

http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/ The academic Deep Learning research community has largely stayed away from the financial markets. Maybe that’s because the finance industry has a bad reputation,…

论文翻译——Character-level Convolutional Networks for Text Classification

论文地址 Abstract Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its sense). 开放文本语义分析器被设计为通过推断相应的意义表示(MR -其意义的正式表示)来解释自然语言中的任何语句.…

(zhuan) Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Evolution Strategies as a Scalable Alternative to Reinforcement Learning this blog from: https://blog.openai.com/evolution-strategies/ MARCH 24, 2017 Evolution Strategies as a Scalable Alternative to Reinforcement Learning We’ve discovered that evo…

Text Classification

Text Classification For purpose of word embedding extrinsic evaluation, especially downstream task. Some concepts are informed from 复旦大学NLP组 Statistical-Based Method Logistic Regression Statistics perspective based text classification described as fo…

（转）Predictive learning vs. representation learning 预测学习与表示学习

Predictive learning vs. representation learning 预测学习与表示学习 When you take a machine learning class, there's a good chance it's divided into a unit on supervised learning and a unit on unsupervised learning. We certainly care about this distinction f…

(转) Deep Learning Research Review Week 2: Reinforcement Learning

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(转) Deep Reinforcement Learning: Playing a Racing Game

Byte Tank Posts Archive Deep Reinforcement Learning: Playing a Racing Game OCT 6TH, 2016 Agent playing Out Run, session 201609171218_175epsNo time limit, no traffic, 2X time lapse Above is the built deep Q-network (DQN) agent playing Out Run, trained…

(转) Deep Reinforcement Learning: Pong from Pixels

Andrej Karpathy blog About Hacker's guide to Neural Networks Deep Reinforcement Learning: Pong from Pixels May 31, 2016 This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatica…