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

PRISE

Feature Extraction Functions for Neural Logic Rule Learningtracting functions for integrating human knowledge abstracted as logic rules into the predictive behaviour of a neural network. These functions are embodied as programming functions, which represent the applicable domain knowledge as a set of logical instructions and provide a modified distribution

知道

Learning High-Resolution Domain-Specific Representations with a GAN Generator this work we study representations learnt by a GAN generator. First, we show that these representations can be easily projected onto semantic segmentation map using a lightweight decoder. We find that such semantic projection can be learnt from just a few annotated images. Based on this finding, we

正论

Predicting Polypharmacy Side Effects Through a Relation-Wise Graph Attention Networkortant to have reliable tools able to predict if the activity of a drug could unfavorably change when combined with others. State-of-the-art methods face this problem as a link prediction task on a multilayer graph describing drug-drug interactions (DDI) and protein-protein interactions (PPI), since

抛媚眼

LGL-GNN: Learning Global and Local Information for Graph Neural Networksgraph classification tasks. Our idea is to concatenate the convolution results of the deep graph convolutional network and the motif-based subgraph convolutional network layer by layer, and give attention weights to global features and local features. We hope that this method can alleviate the over-

争论

Graph Transformer: Learning Better Representations for Graph Neural Networksce on many graph classification tasks. However, most state-of-the-art GNNs face the challenge of the over-smoothing problem and cannot learn latent relations between distant vertices well. To overcome this problem, we develop a novel Graph Transformer (GT) unit to learn latent relations timely. In a

Gesture

Weighted Network Analysis Using the Debye Modelensively used to explore network structure. One of the shortcomings of this model is that it is couched in terms of unweighted edges. To overcome this problem and to extend the utility of this type of analysis, in this paper, we explore how the Debye solid model can be used to describe the probabili

Heterodoxy

Estimating the Manifold Dimension of a Complex Network Using Weyl’s Lawtribution to the way the networks respond to diffusion and percolation processes. In this paper we propose a way to estimate the dimension of the manifold underlying a network that is based on Weyl’s law, a mathematical result that describes the asymptotic behaviour of the eigenvalues of the graph L

恸哭

裂缝

Augmenting Graph Convolutional Neural Networks with Highpass Filters to graph spectral methods, Fourier analysis and graph signal processing. Here, we illustrate the utility of our graph convolutional approach to the classification using citation datasets and knowledge graphs. The results show that our method provides a margin of improvement over the alternative.

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Feature Extraction Functions for Neural Logic Rule Learningnot require any kind of special mathematical encoding, which makes our method very general and flexible in nature. We illustrate the performance of our approach for sentiment classification and compare our results to those obtained using two baselines.