Book ChapterDOI
Real and Complex Analysis
Roger Cooke
- pp 489-507
About:
The article was published on 2011-02-15. It has received 1876 citations till now. The article focuses on the topics: Abel's test & Conjugate Fourier series.read more
Citations
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Journal ArticleDOI
The Graph Neural Network Model
TL;DR: A new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains, and implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space.
Book
Analytic Combinatorics
TL;DR: This text can be used as the basis for an advanced undergraduate or a graduate course on the subject, or for self-study, and is certain to become the definitive reference on the topic.
Journal ArticleDOI
Towards a Mathematical Theory of Super-resolution
TL;DR: In this article, the authors developed a mathematical theory of super-resolution, which is the problem of recovering the details of an object from coarse scale information only from samples at the low end of the spectrum.
MonographDOI
Random Graphs and Complex Networks
TL;DR: This chapter explains why many real-world networks are small worlds and have large fluctuations in their degrees, and why Probability theory offers a highly effective way to deal with the complexity of networks, and leads us to consider random graphs.
Journal ArticleDOI
Update or Wait: How to Keep Your Data Fresh
TL;DR: In this paper, the authors study how to optimally manage the freshness of information updates sent from a source node to a destination via a channel and develop efficient algorithms to find the optimal update policy among all causal policies and establish sufficient and necessary conditions for the optimality of the zero-wait policy.
References
More filters
Journal ArticleDOI
The Graph Neural Network Model
TL;DR: A new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains, and implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space.
Book
Analytic Combinatorics
TL;DR: This text can be used as the basis for an advanced undergraduate or a graduate course on the subject, or for self-study, and is certain to become the definitive reference on the topic.
MonographDOI
Random Graphs and Complex Networks
TL;DR: This chapter explains why many real-world networks are small worlds and have large fluctuations in their degrees, and why Probability theory offers a highly effective way to deal with the complexity of networks, and leads us to consider random graphs.
Posted Content
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios,Eric Nalisnick,Danilo Jimenez Rezende,Shakir Mohamed,Balaji Lakshminarayanan +4 more
TL;DR: This review places special emphasis on the fundamental principles of flow design, and discusses foundational topics such as expressive power and computational trade-offs, and summarizes the use of flows for tasks such as generative modeling, approximate inference, and supervised learning.
Journal ArticleDOI
Model-free control
Michel Fliess,Cédric Join +1 more
TL;DR: Model-free control and the corresponding ‘intelligent’ PID controllers (iPIDs), which already had many successful concrete applications, are presented here for the first time in an unified manner, where the new advances are taken into account.