【博士每天一篇文献-算法】Adult neurogenesis acts as a neural regularizer 本文研究了成人神经发生(adult neurogenesis)在大脑学习过程中的作用,发现其作为一种神经调节器能提高学习泛化能力,并通过在卷积神经网络(CNN)中模拟神经发生,证明了其作为正则化手段与传统技术一样有效,甚至在某些方面更优。 29 6 6 Be...
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。
Graph to Neural Networks论文 摘要 太长不看版 原文翻译 实验重心 将有向无环的MLP转化为双向关系图的消息传递 将生成得到的WS-flex图作为神经架构搜索的map NAS 结论 Graph to Neural Networks论文 原文下载:《Graph Structure of Neural Networks》
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...
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...
【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。