Titlebook: Structural, Syntactic, and Statistical Pattern Recognition; Joint IAPR Internati Andrea Torsello,Luca Rossi,Antonio Robles-Kelly Conference

可觸知

Graph Transformer: Learning Better Representations for Graph Neural Networksnt connections well and form better representations for graphs. Moreover, the proposed Graph Transformer with Mixed Network (GTMN) can learn both local and global information simultaneously. Experiments on standard graph classification benchmarks demonstrate that our proposed approach performs better when compared with other competing methods.

破譯

Estimating the Manifold Dimension of a Complex Network Using Weyl’s Lawlf-similarity. Through an extensive set of experiments on both synthetic and real-world networks we show that our approach is able to correctly estimate the manifold dimension. We compare this with alternative methods to compute the fractal dimension and we show that our approach yields a better estimate on both synthetic and real-world examples.

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格子架

Palpitation

LGL-GNN: Learning Global and Local Information for Graph Neural Networkssmoothing problem when the depth of the neural networks increases, and the introduction of motif for local convolution can better learn local neighborhood features with strong connectivity. Finally, our experiments on standard graph classification benchmarks prove the effectiveness of the model.

輕打

Conference proceedings 20212020, held in Padua, Italy, in January 2021...The 35 papers presented in this volume were carefully reviewed and selected from 81 submissions...The accepted papers cover the major topics of current interest in pattern recognition, including classification and clustering, deep learning, structural ma

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瘋狂

Exponential Weighted Moving Average of Time Series in Arbitrary Spaces with Application to Stringsl case of weighted mean computation. We develop three computation methods. In addition to the direct computation in the original space, we particularly study an approach to embedding the data items of a time series into vector space. The feasibility of our EWMA computation framework is exemplarily demonstrated on strings.

GUMP

暫時休息

0302-9743 s...The accepted papers cover the major topics of current interest in pattern recognition, including classification and clustering, deep learning, structural matching and graph-theoretic methods, and multimedia analysis and understanding..978-3-030-73972-0978-3-030-73973-7Series ISSN 0302-9743 Series E-ISSN 1611-3349