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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...
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...
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, ...
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...
High-order harmonic generation (HHG) from the interaction of ultra-intense laser pulses with atoms is an important tabletop short-wave coherent light source. Accurate quantum simulations of it present...
Nowadays, graph symmetries are becoming a very important and rapidly growing area of study, and often looked at closely by computer scientists and other network designers. One significant instance of ...
The 9th International Conference on Ambient Systems, Networks and Technologies (ANT-2018) is a leading international conference for researchers and industry practitioners to share their new ideas, ori...
This special session deals with deals with the different problems of intelligent wireless sensor networks (WSN) used for monitoring and distributed control. environment, industry, medicine, agricultur...
Nowadays, graph symmetries are becoming a very important and rapidly growing area of study, and often looked at closely by computer scientists and other network designers. One significant instance of ...
The program aims at showing the role of modern statistical methods in complex data and serves to support interactions among mathematicians, statisticians, engineers and scientists working in the inter...
Following the successes of previous events, the 15th International Symposium on Neural Networks (ISNN 2018) will be held in Minsk, Belarus. Located by the Svislach and the Nyamiha Rivers, Minsk is the...

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