【学习】CVPR 2017 Tutorial：如何把深度学习的思想应用到Graph Theory上

2001到2010年间，因为Social Networks的兴起，曾经有一段时间有很多学者热衷于研究Graph Theory，以及Graph Theory在Semi-supervised Learning中的应用，在Community Detection中的应用等等。那么，在深度学习席卷的今天，这些研究又如何和深度学习衔接上呢？在CVPR 2017的一个讲座中，来自瑞士的科学家、Facebook AI研究院的科学家以及新加坡南阳理工大学的科学家共同讲解了如何把Deep Learning（主要是CNN）的思想应用到Graph Theory上，让传统的诸如Spectral Decomposition、Graph Laplacian的工具都和现在的CNN工具相结合。应该说整个讲座略微理论，虽然在一些数据及上（比如Citation Network）上看出来这样的结合有不错的效果，但是从整体上来说，依然没有展现出这一套新理论的实用场景。不过，这个讲座不失为了解一些新工具的机会。

In the past decade, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. However, so far research has mainly focused on developing deep learning methods for Euclidean-structured data. However, many important applications have to deal with non-Euclidean structured data, such as graphs and manifolds. Such geometric data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, recommendation systems, and web applications. The adoption of deep learning in these fields has been lagging behind until recently, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. This website is a collection of materials in the emerging field of geometric deep learning on graphs and manifolds.

链接：

http://geometricdeeplearning.com/

原文链接：

https://m.weibo.cn/3193816967/4138803880844423