End-To-End Memory Networks】的更多相关文章

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本文作者:杨昆霖,2015级本科生,目前研究方向为知识图谱,推荐系统,来自中国人民大学大数据管理与分析方法研究北京市重点实验室. 引言 经常上购物网站时,注意力会被首页上的推荐吸引过去,往往本来只想买一件小商品,但却被推荐商品耗费不少时间与金钱.有时候会在想,虽然推荐商品挺吸引人的,但是它究竟为什么给出这些推荐,背后的原因却往往不得而知.本文将介绍的这篇SIGIR 2018论文提出了新的序列化推荐模型KSR(Knowledge-enhanced Sequential Recommender),利…
End-To-End Memory Networks 2019-05-20 14:37:35 Paper:https://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf Code:https://github.com/facebook/MemNN 1. Background and Motivation: 现在人工智能研究的两个挑战性的问题是:第一是能够构建模型,使其能够进行多个计算步骤,以服务于回答问题或者完成一个任务:另一个是…
一.论文所解决的问题 实现长期记忆(大量的记忆),而且实现怎样从长期记忆中读取和写入,此外还增加了推理功能 为什么长期记忆非常重要:由于传统的RNN连复制任务都不行,LSTM预计也够玄乎. 在QA问题中,长期记忆是非常重要的,充当知识库的作用.从当中获取长期记忆来回答问题 上面这个问题就是,当遇到有若干个句子而且句子之间有联系的时候,RNN和LSTM就不能非常好地解决,以为是长期依赖.须要从记忆中提取信息 二.论文的解决方式 (0)总体架构一览 实际上所谓的Memory Network是一个通用…
今天和陈驰,汪鑫讨论了一下,借此记录一下想法. 关于这篇论文,要弄清的地方有: 1.LSTMtree到底是从上往下还是从下往上学的,再确认一下 2.关于每个节点的标注问题 3.label的值到底该怎么定 4.该神经网络的输入形式到底是什么 今天晚上对照了目前学习的两篇LSTM论文,新的发现: 之所以顺序一个是从上到下,另一个是从下到上,仅仅是因为ht取的东西不同! Improved semantic那一篇:ht取的是孩子节点的信息(目前仅看了child-sum,还需确认N-ary) Top-do…
LSTM’s in Pytorch Example: An LSTM for Part-of-Speech Tagging Exercise: Augmenting the LSTM part-of-speech tagger with character-level features Sequence models are central to NLP: they are models where there is some sort of dependence through time be…
两种形式的LSTM变体 Child-Sum Tree-LSTMs N-ary Tree-LSTMs https://paperswithcode.com/paper/improved-semantic-representations-from-tree…
Attention and Augmented Recurrent Neural Networks CHRIS OLAHGoogle Brain SHAN CARTERGoogle Brain Sept. 8 2016 Citation: Olah & Carter, 2016 Recurrent neural networks are one of the staples of deep learning, allowing neural networks to work with seque…
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks 作者信息:Kai Sheng Tai Stanford UniversityRichard Socher MetaMindChristopher D. Manning Stanford University 数据: 1)Stanford Sentiment Treebank 情感分为五类 2)Sentence Involvi…
Understanding LSTM Networks Recurrent Neural Networks Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and sta…
[论文标题]Collaborative Memory Network for Recommendation Systems    (SIGIR'18) [论文作者]—Travis Ebesu (Santa Clara University).—Bin Shen (Google).—Yi Fang (Santa Clara University) [论文链接]Paper(10-pages // Double column) [摘要] 在现代网络平台上,推荐系统对于保持用户对个性化内容的关注起着至关…
@翻译:huangyongye 原文链接: Understanding LSTM Networks 前言:其实之前就已经用过 LSTM 了,是在深度学习框架 keras 上直接用的,但是到现在对LSTM详细的网络结构还是不了解,心里牵挂着难受呀!今天看了 tensorflow 文档上面推荐的这篇博文,看完这后,焕然大悟,对 LSTM 的结构理解基本上没有太大问题.此博文写得真真真好!!!为了帮助大家理解,也是怕日后自己对这些有遗忘的话可以迅速回想起来,所以打算对原文写个翻译.首先声明,由于本人水…
Adam Kosiorek About Attention in Neural Networks and How to Use It this blog comes from: http://akosiorek.github.io/ml/2017/10/14/visual-attention.html  Oct 14, 2017 Attention mechanisms in neural networks, otherwise known as neural attention or just…
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前言: DNC可以称为NTM的进一步发展,希望先看看这篇译文,关于NTM的译文:人工机器-NTM-Neutral Turing Machine 基于神经网络的混合计算 Hybrid computing using a neural network with dynamic external memory 原文:Nature:doi: 10.1038/nature20101 异义祠:memory matrix :存储矩阵,内存以矩阵方式编码,亦成为记忆矩阵. the neural Turing m…
转自:https://www.jianshu.com/p/e5f2b20d95ff,感谢分享! 基础Memory-network 传统的RNN/LSTM等模型的隐藏状态或者Attention机制的记忆存储能力太弱,无法存储太多的信息,很容易丢失一部分语义信息,所以记忆网络通过引入外部存储来记忆信息.记忆网络的一般框架如下图所示:   记忆网络 它包括四个模块:I(Input),G(Generalization),O(Output),R(Response),另外还包括一些记忆单元用于存储记忆.In…
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目录 1.Memory Networks 框架 流程 损失函数 QA 问题 一些扩展 小结 2.End-To-End Memory Networks Single Layer 输入模块 算法流程 Multiple Layer 网络参数设置细节 QA 问题 3 Key-Value Memory Networks 4 Dynamic Memory Networks Input Module Question Module Episodic Memory Module Attention mechan…
注意力机制之Attention Augmented Convolutional Networks 原始链接:https://www.yuque.com/lart/papers/aaconv 核心内容 We propose to augment convolutional operators with this self-attention mechanism by concatenating convolutional feature maps with a set of feature map…
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