目录 概 主要内容 Locatello F., Bauer S., Lucic M., R"{a}tsch G., Gelly S. Sch"{o}lkopf and Bachem Olivier. Challenging common assumptions in the unsupervised learning of disentangled representations. In International Conference on Machine Leaning (ICML…

这篇文章是ICML 2019上一篇做域适应的文章,无监督域适应研究的问题是如何把源域上训练的模型结合无lable的目标域数据使得该模型在目标域上有良好的表现.之前的研究都有个假设,就是数据来自哪个域是有着域标签的,其实这不太现实,就拿手写字识别打比方,不同的人使用不同的笔如纸张,那写出来的字会是不同的域的,识别的时候不可能模型还得需要知道待识别的字来自哪个域.这篇文章研究的内容是如何把有标注的源域信息迁移到无标注的任意目标域数据上.相当于是从1个源域到N个目标域的迁移,而一些之前的论文的假设是目…

Unsupervised Learning of Video Representations using LSTMs Note here: it's a learning notes on new LSTMs architecture used as an unsupervised learning way of video representations. (More unsupervised learning related topics, you can refer to: Learnin…

Unsupervised Learning of Visual Representations using Videos Note here: it's a learning note on Prof. Gupta's novel work published on ICCV2015. It's really exciting to know how unsupervised learning method can contribute to learn visual representatio…

Unsupervised learning refers to data science approaches that involve learning without a prior knowledge about the classification of sample data. In Wikipedia, unsupervised learning has been described as "the task of inferring a function to describe h…

1    Unsupervised Learning 1.1    k-means clustering algorithm 1.1.1    算法思想 1.1.2    k-means的不足之处 1.1.3    如何选择K值 1.1.4    Spark MLlib 实现 k-means 算法 1.2    Mixture of Gaussians and the EM algorithm 1.3    The EM Algorithm 1.4    Principal Components…

Unsupervised Learning: Use Cases Contents Visualization K-Means Clustering Transfer Learning K-Nearest Neighbors The features learned by deep neural networks can be used for the purposes of classification, clustering and regression. Neural nets are s…

Supervised Learning In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output. Supervised learning problems are categorized…

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables. We can derive this structure by clustering t…

PredNet --- Deep Predictive coding networks for video prediction and unsupervised learning   ICLR 2017  2017.03.12  Code and video examples can be found at: https://coxlab.github.io/prednet/ 摘要:基于监督训练的深度学习技术取得了非常大的成功,但是无监督问题仍然是一个未能解决的一大难题(从未标注的数据中学习到…

@(131 - Machine Learning | 机器学习) 零. Goal How Unsupervised Learning fills in that model gap from the original Machine Learning work flow 2.How to compare different models developed using Unsupervised Learning for their relative strengths and relative…

Unsupervised learning, attention, and other mysteries Get notified when our free report “Future of Machine Intelligence: Perspectives from Leading Practitioners” is available for download. The following interview is one of many that will be included…

8 Unsupervised Learning8.1 Clustering8.1.1 Unsupervised Learning: Introduction集群(聚类)的概念.什么是无监督学习:对于无标记无关联标记的数据,要求算法分析出数据的结构.什么是聚类:将未加标签的数据分成有紧密关系的子集或者簇. 做道题: ABC 8.1.2 K-Means Algorithm讨论什么是K均值以及K均值的使用.K均值算法是一种迭代的聚类方法. 用图来展示K均值更加直观:1.因为本例要将数据分为2类,所以要…

无监督学习(Unsupervised Learning) 聚类无监督学习 特点 只给出了样本, 但是没有提供标签 通过无监督学习算法给出的样本分成几个族(cluster), 分出来的类别不是我们自己规定的, 而是无监督学习算法自己计算出来的 K-means 聚类算法 规定 \(c^{(i)}\): 表示\(x^{(i)}\)属于哪个cluster, 如\(x^{(1)}\)属于\(c^{(1)}\)簇, 如果\(c^{(1)}=1\), 则\(x^{(1)}\)划分在第1个类别 \(\mu_k\…

文章:Deep Clustering for Unsupervised Learning of Visual Features 作者:Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze 来自于:Facebook AI Research 发表于:ECCV 2018 目录 •相关链接 •相关方法介绍 •文章出发点 •文章亮点与贡献 •方法细节 •实验结果 •分析与总结 相关链接 论文:https://arxiv.or…

unsupervised learning 上面是监督学习与无监督学习的比较,监督学习的training set是一组带label(y)的训练集,而无监督学习不带有label(y). 上图中的监督学习求出决策线,用来区别正负样本点: clustering是unsupervised learning算法的一种,用来确定数据内部的结构. clustering算法的一些应用 对客户进行分组clustering来有针对性的营销: 对社交网络(如facebook等)进行分析,找出朋友圈: 利用cluste…

摘要: 本文是吴恩达 (Andrew Ng)老师<机器学习>课程,第一章<绪论:初识机器学习>中第4课时<无监督学习>的视频原文字幕.为本人在视频学习过程中逐字逐句记录下来以便日后查阅使用.现分享给大家.如有错误,欢迎大家批评指正,在此表示诚挚地感谢!同时希望对大家的学习能有所帮助. In this video (article), we'll talk about the second major type of machine learning problem, c…

监督学习即是supervised learning,原始数据中有每个数据有自己的数据结构同时有标签,用于classify,机器learn的是判定规则,通过已成熟的数据training model达到判断新点类型的目的. 非监督学习即是unsupervised learning,原始数据中没有附加标签,仅有数据结构,cluster的过程是机器发现相似数据结构先去找相似pattern,没有新加入的数据,仅是对原始数据的描述.  …

一.基本信息 论文题目:<DeepWalk: Online Learning of Social Representations>发表时间:  KDD 2014论文作者:  Bryan Perozzi.Rami Al-Rfou.Steven Skiena论文地址:  https://dl.acm.org/citation.cfm?id=2623732 二.前言 普通的邻接矩阵在存储的关系很多时,纬度将变得很高,而进行矩阵分解是一个相当费时复杂的过程,因此通过矩阵分解的方法进行网络的表示学习,目…

Learning Cross-Modal Deep Representations for Robust Pedestrian Detection 2017-04-11  19:40:22  Motivation: 本文主要是考虑了在光照极端恶劣的情况下,如何充分的利用 thermal data 进行协助学习提升 可见光图像的 特征表达能力,而借鉴了 ICCV 2015 年的一个文章,称为:监督迁移的方法,以一种模态的特征为 label,以监督学习的方式实现无监督学习.说到这里可能比较让人糊涂,…

中文译文:深度学习.自然语言处理和表征方法 http://blog.jobbole.com/77709/ 英文原文:Deep Learning, NLP, and Representations http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/ 总结: 这篇文章中主要提到了单层神经网络,单词嵌入(word embeddings),表征这几个概念,结合具体的实例,写的是通俗易懂,在引用参考文献的位置都给出了对应的链接,一些…

机器人领域顶级会议 ICRA 2019 正在加拿大蒙特利尔举行(当地时间 5 月 20 日-24 日),刚刚大会公布了最佳论文奖项,来自斯坦福大学李飞飞组的研究<Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks>获得了最佳论文. 图源:https://twitter.com/animesh_garg/status/1131…

1 题目 <A Simple Framework for Contrastive Learning of Visual Representations> 作者: Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton 2 介绍 本文主要介绍 SimCLR框架. 定义: SimCLR:一个简单的视觉表示对比学习框架,不仅比以前的工作更出色,而且也更简单,既不需要专门的架构,也不需要储存库. 性能: 在 $ImageNet$ 上大…

目录 概 主要内容 流程 projection head g constractive loss augmentation other 代码 Chen T., Kornblith S., Norouzi M., Hinton G. A Simple Framework for Contrastive Learning of Visual Representations. arXiv: Learning, 2020. @article{chen2020a, title={A Simple Fram…

论文信息 论文标题:Accurate Learning of Graph Representations with Graph Multiset Pooling论文作者:Jinheon Baek, Minki Kang, Sung Ju Hwang论文来源:2021, ICLR论文地址:download 论文代码:download 1 Introduction 图池化存在的问题:获得的图表示需进一步使用池化函数将一组节点表示映射为紧凑的形式.对所有节点表示的简单求和或平均都平等地考虑所有节点特征…

Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal" algorithms it is the data that "tells" what the "good answer" is. Example: an hypothetical non-machine learning algorithm for face recogniti…

1. Abstract 提出了一种无监督单目深度估计和相机运动估计的框架 利用视觉合成作为监督信息,使用端到端的方式学习 网络分为两部分(严格意义上是三个) 单目深度估计 多视图姿态估计 解释性网络(论文后面提到训练了第三个网络) 2. Introduction 计算机几何视觉难以重建真实的场景模型 由于非刚性.遮挡.纹理缺失等情况的存在 人类在很短的时刻可以推断自我运动以及三维场景的结构,为什么? 一个假设就是人类在移动中通过观察大量的场景,已经进化出一个对真实世界丰富的.具有结构层次的理解力…

@(131 - Machine Learning | 机器学习) PCA是一种特征选择方法,可将一组相关变量转变成一组基础正交变量 25 PCA的回顾和定义 Demo: when to use PCA latent features driving the patterns in the data (demo find the big shots in enron) 访问隐藏的特征 dimensionality reduction 1)visualize high dimensional dat…

1 Why? Reason1 Knowledge Discovery (about human beings limitaitons) Reason2 Cause of Dimensionality (维度灾难) (about ML algorithm itself) 所需的数据量会根据你所拥有的特征数量以指数速度增长 2 NP-Hard Problem arbitrarily choose m features from n features (m≤n),don't know what m t…

@(131 - Machine Learning | 机器学习) 1 Feature Scaling transforms features to have range [0,1] according to the formula $x' = \frac{x-x_{min}}{x_{max}-x_{min}} $ 1.1 Sklearn - MinMaxScaler from sklearn.preprocessing import MinMaxScaler import numpy weigh…