Graph Structure of Neural Networks(ICML 2020) 原文链接:Graph Structure of Neural Networks(ICML 2020) 神经网络经常表示为神经元之间的连接图。然而,尽管它们被广泛使用,通过之前的研究发现,神经网络的性能在很大程度上取决于它的结构,但对神经网络的准确性和其底层图结构之间的关系几乎没有系统的理解。本文系统地...
Here we systematically investigate how does the graph structure of neural networks affect their predictive performance. To this end, we develop a novel graph-based representation of neural networks called relational graph, where layers of neural network computation correspond to rounds of message ...
graph在计算中有三个挑战:lack of consistent structure、node-order equivariance和scalability。 Lack of Consistent Structure graph是极其灵活的数学模型,同时这意味着它们缺乏跨实例的一致结构。比如不同分子之间有不同的结构。用一种可以计算的格式来表示graph并不是一件简单的事情,graph的最终表示通常由实际问题决定。
We define a novel graph-based representation of neural networks calledrelational graph, as opposed to the commonly usedcomputational graph. We then systematically investigate how does the graph structure of neural networks affect their predictive performance. The key finding is that asweet spotof relati...
【2019/ICML】DAG-GNN: DAG Structure Learning with Graph Neural Networks,程序员大本营,技术文章内容聚合第一站。
graph在计算中有三个挑战:lack of consistent structure、node-order equivariance和scalability。 Lack of Consistent Structure graph是极其灵活的数学模型,同时这意味着它们缺乏跨实例的一致结构。比如不同分子之间有不同的结构。用一种可以计算的格式来表示graph并不是一件简单的事情,graph的最终表示通常由实际问题决定...
We present an alternative to Graph Neural Networks (GNNs) termed Graph Structured Neural Networks (GSNN), which uses cell signaling knowledge, encoded as a graph data structure, to add inductive biases to deep learning. We apply our method to perturbation biology using the LINCS L1000 dataset ...
In this section, we describe in detail how we design and explore the space of relational graphs defined in Section 2, in order to study the relationship between the graph structure of neural networks and their predictive performance. Three main components are needed to make progress: (1) graph...
graph structure of neural networks affect their predictive performance. To this end, we develop a novel graph-based representation of neural networks called relational graph, where layers of neural network computation correspond to rounds of message exchange along the graph structure. Using this ...
Petar Veličković的博士论文去年就已经完成,只是最近才跟大家分享。这篇论文的题目是《The resurgence of structure in deep neural networks》,共计 147 页,涵盖了 Petar Veličković的上述经典工作和其他关于图神经网络的内容,非常值得一读。 论文链接:https://www.repository.cam.ac.uk/handle/1810/292230...