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图神经网络(Graph Neural Network,GNN)是指使用神经网络来学习图结构数据,提取和发掘图结构数据中的特征和模式,满足聚类、分类、预测、分割、生成等图学习任务需求的算法总称。如何从图数据(网络数据)中学习有效的数据表征是大数据与人工智能时代的一大挑战。为此,本次汇报将从一种特殊的谱图神经网络(图卷积神经网络)出发,介绍相应的消息传递范式,揭示传统机器学习和图神经网络的内在关联,并...
近日,实验室2021级博士研究生刘婧逸的学术论文《SNR: Symbolic Network-based Rectifiable Learning Framework for Symbolic Regression》被人工智能顶级期刊Neural Networks录用。《Neural Networks》是世界上三个最古老的神经建模学会的档案期刊:国际神经网络学会(INNS)、欧洲神经网络学会(EN...
Cornell researchers have developed an optical neural network (ONN) that can filter relevant information from a scene before the visual image is detected by a camera, a method that may make it possible...
In the vast, expansive skies where birds once ruled supreme, a new crop of aviators is taking flight. These pioneers of the air are not living creatures, but rather a product of deliberate innovation:...
Parameter estimation or filtering is one of the important issues in diverse fields including statistical learning, signal processing, system identification and adaptive control. With the development o...
We extend the concept of self-consistency for the Fokker-Planck equation (FPE) [Shen et al., 2022] to the more general McKean-Vlasov equation (MVE). While FPE describes the macroscopic behavior of par...
Neural networks, a type of machine-learning model, are being used to help humans complete a wide variety of tasks, from predicting if someone’s credit score is high enough to qualify for a loan to dia...
This report consists of two parts associated with graph neural networks: generalization and graph structural learning. We first study the Rademacher complexity of GNNs, as one of independent-algorithm...
When two spacecrafts need to bridge a connection in orbit they dock. This means the onboard computers controlling their thrusters need unfettered communication between one another that cannot be disru...
We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over...
Modern neural networks are usually over-parameterized—the number of parameters exceeds the number of training data. In this case the loss functions tend to have many (or even infinite) global minima, ...
In this talk, we discuss the realization of Boolean control networks (BCNs) and apply obtained results to some decoupling problems. It is proved that the minimum realization (MR) of a BCN exists uniqu...
We examines China's domestic production networks. It uses VAT invoices to build inter-provincial input-output tables for 2002 and 2012. These are combined with population censuses to determine the loc...
Deep neural networks, as a powerful system to represent high dimensional complex functions, play a key role in deep learning. Convergence of deep neural networks is a fundamental issue in building the...
Networks arise in many areas of research and applications, which come in all shapes and sizes. The most studied and best understood are static network models. Many other network models are also in exi...

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