Intro
Preface
Organization
Contents
Realization of Autoencoders by Kernel Methods
1 Introduction
2 Related Work
3 Autoencoders by Kernel Methods
3.1 Encoder and Decoder
3.2 Fundamental Mapping Without Loss
3.3 Kernelized Autoencoder
4 Comparison with Neural Networks
5 Applications
5.1 Denoising Autoencoders
5.2 Generative Autoencoders
6 Discussion
7 Conclusion
References
Maximal Independent Vertex Set Applied to Graph Pooling
1 Introduction
2 Related Work
2.1 Graph Pooling
3 Proposed Method

3.1 Maximal Independent Vertex Set (MIVS)
3.2 Adaptation of MIVS to Deep Learning
4 Experiments
4.1 Datasets
4.2 Model Architecture and Training Procedure
4.3 Ablation Studies
4.4 Comparison of MIVSPool According to Other Methods
5 Conclusion
References
Annotation-Free Keyword Spotting in Historical Vietnamese Manuscripts Using Graph Matching
1 Introduction
2 Kieu Database
3 Annotation-Free Keyword Spotting (KWS)
3.1 Synthetic Dataset Creation
3.2 Character Detection
3.3 Graph Extraction
3.4 Graph Matching
3.5 Keyword Spotting (KWS)

4 Experimental Evaluation
4.1 Task Setup and Parameter Optimization
4.2 Results
4.3 Ablation Study
5 Conclusions
References
Interactive Generalized Dirichlet Mixture Allocation Model
1 Introduction
2 Model Description
3 Variational Inference
4 Interactive Learning Algorithm
5 Experimental Results
6 Conclusion
References
Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment
1 Introduction
2 Related Work
3 Features Soft-Alignment Graph Neural Networks
4 Experiments
4.1 Experimental Setup
4.2 Ablation Study

4.3 Graph Classification Results
4.4 Graph Regression Results
5 Conclusion
References
Review of Handwriting Analysis for Predicting Personality Traits
1 Introduction
1.1 History
1.2 Applications
1.3 Requirements
2 Research Progress
2.1 Advantages
2.2 Disadvantages
3 Research Steps
3.1 Database
3.2 Pre-processing
3.3 Feature Extraction
3.4 Personality Trait
3.5 Prediction Model
3.6 Performance Measurement
4 Experiment and Future Work
4.1 Experiment
4.2 Future Work
References

Graph Reduction Neural Networks for Structural Pattern Recognition
1 Introduction and Related Work
2 Graph Matching on GNN Reduced Graphs
2.1 Graph Reduction Neural Network (GReNN)
2.2 Classification of GReNN Reduced Graphs
3 Empirical Evaluations
3.1 Datasets and Experimental Setup
3.2 Analysis of the Structure of the Reduced Graphs
3.3 Classification Results
3.4 Ablation Study
4 Conclusions and Future Work
References
Sentiment Analysis from User Reviews Using a Hybrid Generative-Discriminative HMM-SVM Approach
1 Introduction
2 Related Work