HEER-Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks

来源:KDD 2018 原文:HEER code:https://github.com/GentleZhu/HEER 注: 若有错误,欢迎指正这篇KDD’18的文章,没有按照常规的方法将所有的node嵌入到同一的空间,因为文章提出 node 因为连接的 edge 类型(type)不同,存在不兼容(incompatibility)的特性,所以最好能够根据不同的edge type来定义不同度量空间(metric space),保持同一个度量空间下,node的兼容性. 1. Abstract…

[论文阅读笔记] Adversarial Learning on Heterogeneous Information Networks

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论文解读（GraRep）《GraRep: Learning Graph Representations with Global Structural Information》

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论文阅读：Relation Structure-Aware Heterogeneous Information Network Embedding

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论文解读（ N2N）《Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization》

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课程一(Neural Networks and Deep Learning)，第四周（Deep Neural Networks） —— 3.Programming Assignments: Deep Neural Network - Application

Deep Neural Network - Application Congratulations! Welcome to the fourth programming exercise of the deep learning specialization. You will now use everything you have learned to build a deep neural network that classifies cat vs. non-cat images. In…

课程一(Neural Networks and Deep Learning)，第四周（Deep Neural Networks）——2.Programming Assignments: Building your Deep Neural Network: Step by Step

Building your Deep Neural Network: Step by Step Welcome to your third programming exercise of the deep learning specialization. You will implement all the building blocks of a neural network and use these building blocks in the next assignment to bui…

课程一(Neural Networks and Deep Learning)，第四周（Deep Neural Networks）—— 0.学习目标

Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision. 学习目标 See deep neural networks as successive blocks put one after each other Build and train a deep L-layer Ne…

（原创）Stanford Machine Learning (by Andrew NG) --- (week 4) Neural Networks Representation

Andrew NG的Machine learning课程地址为:https://www.coursera.org/course/ml 神经网络一直被认为是比较难懂的问题,NG将神经网络部分的课程分为了两个星期来介绍,可见Neural Networks内容之多.言归正传,通过之前的学习我们知道,使用非线性的多项式能够帮助我们建立更好的分类模型.但当遇特征非常多的时候,需要训练的参数太多,使得训练非常复杂,使得逻辑回归有心无力. 例如我们有100个特征,如果用这100个特征来构建一个非线性的多项式模…

（原创）Stanford Machine Learning (by Andrew NG) --- (week 5) Neural Networks Learning

本栏目内容来自Andrew NG老师的公开课:https://class.coursera.org/ml/class/index 一般而言, 人工神经网络与经典计算方法相比并非优越, 只有当常规方法解决不了或效果不佳时人工神经网络方法才能显示出其优越性.尤其对问题的机理不甚了解或不能用数学模型表示的系统,如故障诊断.特征提取和预测等问题,人工神经网络往往是最有利的工具.另一方面, 人工神经网络对处理大量原始数据而不能用规则或公式描述的问题, 表现出极大的灵活性和自适应性. 神经网络模型解决问题的…

Coursera, Deep Learning 5, Sequence Models, week1 Recurrent Neural Networks

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课程一(Neural Networks and Deep Learning)，第四周（Deep Neural Networks）—— 1.Practice Questions: Key concepts on Deep Neural Networks

[解释] [解释] 比如算法中的learing rateα(学习率).iterations(梯度下降法循环的数量).L(隐藏层数目).n[l] (隐藏层单元数目).choice of activation function(激活函数的选择)都需要你来设置,这些数字实际上控制了最后的参数W和b的值,所以它们被称作超参数. [解释] 在深度神经网络的这许多隐藏层中,较早的前几层能学习一些低层次的简单特征,等到后几层,就能把简单的特征结合起来,去探测更加复杂的东西.比如你录在音频里的单词.词组或是句子…

近年Recsys论文

2015年~2017年SIGIR,SIGKDD,ICML三大会议的Recsys论文: [转载请注明出处:https://www.cnblogs.com/shenxiaolin/p/8321722.html] SIGIR-2015 [Title]WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation [Abstract]Matrix approximation…

KDD2015,Accepted Papers

Accepted Papers by Session Research Session RT01: Social and Graphs 1Tuesday 10:20 am–12:00 pm | Level 3 – Ballroom AChair: Tanya Berger-Wolf Efficient Algorithms for Public-Private Social NetworksFlavio Chierichetti,Sapienza University of Rome; Ales…

KDD2016,Accepted Papers

RESEARCH TRACK PAPERS - ORAL Title & Authors NetCycle: Collective Evolution Inference in Heterogeneous Information NetworksAuthor(s): Yizhou Zhang*, Fudan University; Xiong Yun, ; Xiangnan Kong, Worcester Polytechnic Institute; Yangyong Zhu, Fudan Un…

KDD-CUP Proposal

From 鞠源已有 1303 次阅读 2012-11-25 21:09 |系统分类:科研笔记|关键词:会议领域 justify 知识 KDDCUP - Competition is a strong mover for Science and Engineering ACM KDD是知识发现和数据挖掘领域的顶级会议,KDD CUP又是基于ACM KDD的世界级赛事.目的在于1. 探求从海量数据中挖掘出高层知识的最佳方法.2. 作为学术界和工业界沟通的桥梁(事实上KDD从97起,首先是由工业界…

WSDM 2014推荐系统论文

Xiao Yu, Hao Ma, Paul Hsu, Jiawei Han On Building Entity Recommender Systems Using User Click Log and Freebase Knowledge Alexander Smola, Yuchen Zhang, Amr Ahmed, Vanja Josifovski Taxonomy Discovery for Personalized Recommendation Steffen Rendle, Chr…

What are some good books/papers for learning deep learning?

What's the most effective way to get started with deep learning? 29 Answers Yoshua Bengio, My lab has been one of the three that started the deep learning approach, back in 2006, along with Hinton's... Answered Jan 20, 2016 Originally Ans…

ICLR 2014 International Conference on Learning Representations深度学习论文papers

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零次学习（Zero-Shot Learning）入门(转)

很久没有更文章了,主要是没有找到zero-shot learning(ZSL)方面我特别想要分享的文章,且中间有一段时间在考虑要不要继续做这个题目,再加上我懒 (￢_￢),所以一直拖到了现在. 最近科研没什么进展,就想着写一个ZSL的入门性的文章,目的是为了帮助完全没有接触过这方面,并有些兴趣的同学,能在较短的时间对ZSL有一定的认识,并且对目前的发展情况有一定的把握. 在此之前,需要提到的是:无论是论文笔记,还是总结性的读物,都包含了作者自己的理解和二次加工,想要做出好的工作必定需要自己看论文…

【机器学习Machine Learning】资料大全

昨天总结了深度学习的资料,今天把机器学习的资料也总结一下(友情提示:有些网站需要"科学上网"^_^) 推荐几本好书: 1.Pattern Recognition and Machine Learning (by Hastie, Tibshirani, and Friedman's ) 2.Elements of Statistical Learning(by Bishop's) 这两本是英文的,但是非常全,第一本需要有一定的数学基础,第可以先看第二本.如果看英文觉得吃力,推荐看一下下面…

(转) Awesome Deep Learning

Awesome Deep Learning Table of Contents Free Online Books Courses Videos and Lectures Papers Tutorials Researchers WebSites Datasets Frameworks Miscellaneous Contributing Free Online Books Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Cou…

机器学习(Machine Learning)&深度学习(Deep Learning)资料【转】

转自:机器学习(Machine Learning)&深度学习(Deep Learning)资料 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最…

Deep Learning for NLP 文章列举

Deep Learning for NLP 文章列举原文链接:http://www.xperseverance.net/blogs/2013/07/2124/ 大部分文章来自: http://www.socher.org/ http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial 包括从他们里面的论文里找到的related work Word Embedding Learnig SENNA原始论文[ACL'07]Fas…

How do I learn machine learning?

https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644 How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…

转 Deep Learning for NLP 文章列举

原文链接:http://www.xperseverance.net/blogs/2013/07/2124/ 大部分文章来自: http://www.socher.org/ http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial 包括从他们里面的论文里找到的related work Word Embedding Learnig SENNA原始论文[ACL'07]Fast Semantic Extraction Using…

Wide and Deep Learning Model

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My deep learning reading list

My deep learning reading list 主要是顺着Bengio的PAMI review的文章找出来的.包括几本综述文章,将近100篇论文,各位山头们的Presentation.全部都可以在google上找到.BTW:由于我对视觉尤其是检测识别比较感兴趣,所以关于DL的应用主要都是跟Vision相关的.在其他方面比如语音或者NLP,很少或者几乎没有.个人非常看好CNN和Sparse Autoencoder,这个list也反映了我的偏好,仅供参考. Review Book Lis…

ICLR 2013 International Conference on Learning Representations深度学习论文papers

ICLR 2013 International Conference on Learning Representations May 02 - 04, 2013, Scottsdale, Arizona, USA ICLR 2013 Workshop Track Accepted for Oral Presentation Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Hamsa Sr…