论文《DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017)》对于一个基于CTR预估的推荐系统，最重要的是学习到用户点击行为背后隐含的特征组合。在不同的推荐场景中，低阶组合特征或者高阶组合特征可能都会对最终的CTR产生影响。但是现存的方法总是忽视了高阶或低阶组合特征的联系，或者要求专门的特征工程，因此作者建立了DeepFM模型，将FM与DNN结合起来。
基于上下文的编码器：图像修复的特征学习
We present an unsupervised visual feature learning algo- rithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders – a con- volutional neural network trained to generate the conten
Networks are everywhere. Popular examples include social networks, the hyperlinked World Wide Web, transportation
networks, electricity power networks and
biological gene networks. Networks are
typically represented as a graph whose
vertices repr
Graphs are essential representations of many real-world data such
as social networks. Recent years have witnessed the increasing efforts made to extend the neural network models to graph-structured
data. These methods, which are usually known as th
Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However,
an open challenge in this area is developing techniques that can go beyond simple
edge prediction
这是我3.13-3.20这一周的学习情况，学习李宏毅老师的几个深度学习视频，同时我在学习两篇论文《一种硬盘故障预测的非监督对抗学习方法》和《Lifelong Disk Failure Prediction via GAN-based Anomaly Detection》后的一个文章脉络及内容分析，也就是总结收获啦，PPT的图片档已经上传至博客中，大家可以翻看到，但是会有不少动画在PPT中帮助理解，如果需要，下载PPT就好了，如有不对的地方，请指正，共同学习进步
DTI预测领域的高被引lw，发表于2018年.
Abstract
Motivation
The identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have fo
