Advances in Knowledge Discovery and Data Mining

Name: Advances in Knowledge Discovery and Data Mining | 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part II
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28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7-10, 2024, Proceedings, Part II

De-Nian Yang Xing Xie Vincent S. Tseng Jian Pei Jen-Wei Huang Jerry Chun-Wei Lin(Herausgeber*in)

Springer (Verlag)

Erschienen am 24. April 2024

XXXIV, 459 Seiten

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Inhalt

Intro
General Chairs' Preface
PC Chairs' Preface
Organization
Contents - Part II
Deep Learning
AdaPQ: Adaptive Exploration Product Quantization with Adversary-Aware Block Size Selection Toward Compression Efficiency
1 Introduction
2 Related Works
3 Preliminary
4 Methodology
4.1 Adaptive Exploration Quantization
4.2 Adversary-Aware Block Size Selection
5 Experiments
6 Conclusion
References
Ranking Enhanced Supervised Contrastive Learning for Regression
1 Introduction
2 Related Work
3 Preliminaries
4 Methodology
4.1 Motivation
4.2 Ranking Enhanced Supervised Contrastive Learning (RESupCon)
5 Experiments
5.1 Datasets
5.2 Baselines and Settings
5.3 Overall Performance
5.4 Comparison on Spearman's Rank Correlation Coefficients
5.5 Parameter Study and Loss Curve
6 Conclusion
References
Treatment Effect Estimation Under Unknown Interference
1 Introduction
2 Related Work
3 Preliminaries
4 Proposed Method: Treatment Effect Estimation Under Unknown Interference
4.1 Covariate Representation Learner
4.2 Graph Structure Learner
4.3 Aggregation Function
4.4 Outcome Predictors and ITE Estimators
5 Experiments
5.1 Experiment Settings
5.2 Results
6 Conclusion
A Identifiability of the Expectation of Potential Outcomes
B HSIC
C Implementation Details
D Ablation Experiments
References
A New Loss for Image Retrieval: Class Anchor Margin
1 Introduction
2 Related Work
3 Method
4 Experiments
4.1 Datasets
4.2 Experimental Setup
4.3 Results
5 Conclusion
References
Personalized EDM Subject Generation via Co-factored User-Subject Embedding
1 Introduction
2 Related Work
3 Proposed Model
3.1 Retrieve and Re-rank
3.2 Variational Encoder and Bi-directional Selective Encoder
3.3 User-Subject Co-factor System
3.4 User-Based Decoder
4 Experimental Results
4.1 Quantitative Results
4.2 Effect of Template
5 Conclusions and Future Work
References
Spatial-Temporal Bipartite Graph Attention Network for Traffic Forecasting
1 Introduction
2 Related Work
3 Definitions and Problem Statement
3.1 Definitions
3.2 Problem Statement
4 Methodology
4.1 Data Inputs and Data Preprocessing
4.2 Encoder Decoder Architecture
4.3 Bipartite Graph Attention Layer
4.4 Heterogeneous Cross Attention Layers
5 Experiments
5.1 Experiment Setup
5.2 Comparison of Performance
5.3 Ablation Study
6 Conclusion and Future Works
References
CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation
1 Introduction
2 Related Work
3 Datasets
4 Method
4.1 Pre-training Model
4.2 Medical Dialogue Generation Model
5 Experiments
5.1 Experimental Setting
5.2 Experimental Results
6 Conclusion
References
MvRNA: A New Multi-view Deep Neural Network for Predicting Parkinson's Disease
1 Introduction
2 Related Work
3 Methods
3.1 Data Representation Based on Multiple Views
3.2 ResNet18 with BWH
3.3 Channel Attention Implemented Using SENet
4 Experiments and Results
4.1 Dataset
4.2 Experimental Settings
4.3 Experimental Results and Analysis
4.4 Ablation Experiment
5 Conclusion
References
Path-Aware Cross-Attention Network for Question Answering
1 Introduction
2 Related Work
3 Task Definition
4 Method
4.1 Text Encoder and Path Encoder
4.2 Path-Aware Cross-Attention
4.3 Self-learning Based Path Scoring Method
4.4 Learning and Inference
5 Experiment
5.1 Dataset
5.2 Baseline Models
5.3 Main Result
6 Analysis
6.1 Ablation Studies
6.2 Model Interpretability
6.3 Quantitative Analisis
7 Conclusion
References
StyleAutoEncoder for Manipulating Image Attributes Using Pre-trained StyleGAN
1 Introduction
2 Related Work
3 Methodology
3.1 Preliminaries
3.2 StyleAutoEncoder
3.3 Discussion
4 Experiments
4.1 Evaluation Metrics
4.2 Models Implementation
4.3 Manipulation of Facial Features
4.4 Evaluation on Animal Faces
5 Conclusion
References
SEE: Spherical Embedding Expansion for Improving Deep Metric Learning
1 Introduction
2 Related Works
3 Method
3.1 Preliminary
3.2 Spherical Embedding Expansion
4 Experiments
4.1 Experiment Setting
4.2 Quantitative Results
4.3 Ablation Studies
5 Conclusion
References
Multi-modal Recurrent Graph Neural Networks for Spatiotemporal Forecasting
1 Introduction
2 Related Work
3 Methods
3.1 Problem Formulation
3.2 Model Design
4 Experiments
4.1 Model Baselines
4.2 Primary Results
4.3 Ablation Study
5 Conclusions
7 Appendix
References
Layer-Wise Sparse Training of Transformer via Convolutional Flood Filling
1 Introduction
2 Background and Related Work
2.1 Transformer
2.2 Related Work on Sparse Attention
3 Motivation: Analysis of Sparse Patterns in MHA
4 SPION: Layer-Wise Sparse Attention in Transformer
4.1 Overview of SPION
4.2 Sparsity Pattern Generation with Convolutional Flood Fill Algorithm
5 Experimental Evaluation
5.1 Performance Evaluation
5.2 Computational Complexity Analysis
6 Conclusion
References
Towards Cost-Efficient Federated Multi-agent RL with Learnable Aggregation
1 Introduction
2 Preliminary
3 Federated MARL with Learnable Aggregation
4 Convergence Analysis
5 Experiments
6 Related Work
7 Conclusion
References
LongStory: Coherent, Complete and Length Controlled Long Story Generation
1 Introduction
2 Related Works
2.1 Neural Story Generation
2.2 Recursive Models
2.3 Autometic Metrics
3 Methodology
3.1 Task Description
3.2 Long and Short Term Contexts Weight Calibrator(CWC)
3.3 Long Story Structural Positions (LSP)
3.4 Base Pretrained Model
4 Experiments
4.1 Experiments Set-Up
4.2 Experimental Results
4.3 Further Analysis
5 Conclusion
References
Relation-Aware Label Smoothing for Self-KD
1 Introduction
2 Related Work
3 Our Approach
3.1 RAS-KD
4 Experimental Results
5 Ablation Study
6 Conclusion
References
Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference
1 Introduction
2 Relevant Work
3 Our Bi-CryptoNets
3.1 The Bi-branch of Neural Network
3.2 The Unidirectional Connections
3.3 The Feature Integration
4 Knowledge Distillation for Bi-CryptoNets
5 Experiments
6 Conclusion
References
Enhancing YOLOv7 for Plant Organs Detection Using Attention-Gate Mechanism
1 Introduction
2 Related Work
2.1 Attention-Gate Mechanism
3 YOLOv7 with Attention-Gate Mechanism
4 Experiments
4.1 Experiment Materials
4.2 Evaluation Metrics
4.3 Experimental Results
5 Conclusion
References
On Dark Knowledge for Distilling Generators
1 Introduction
2 Preliminary
3 Theoretical Analysis of Dark Knowledge in Distilling the Generator
3.1 Dark Knowledge of Generators
3.2 Distillation Empirical Risk
3.3 Generalization of the Student Generator
3.4 Impact of Probability Approximation
4 DKtill: Extracting Dark Knowledge for Training Student Generator
4.1 Extracting from Probabilistic Generators
4.2 Extracting from Non-probabilistic Generators
5 Empirical Illustration
5.1 Setting
5.2 Distilling Probabilistic Generators
5.3 Distilling Non-probabilistic Generators
5.4 Small Generators Through DKtill
6 Related Work
7 Conclusion
References
RPH-PGD: Randomly Projected Hessian for Perturbed Gradient Descent
1 Introduction
2 Preliminary
2.1 Notation
2.2 Methods to Escape from Saddle Points
2.3 Perturbed Gradient Descent
3 Algorithms
3.1 Randomly Projected Hessian
3.2 Shifted Randomly Projected Hessian
3.3 RPH-PGD
4 Experiments
5 Conclusion and Future Work
References
Transformer based Multitask Learning for Image Captioning and Object Detection
1 Introduction
2 Related Work
3 Proposed Method
3.1 Objective Function
4 Experimental Setup
5 Results
5.1 Comparison and Analysis
5.2 Ablation Studies
6 Conclusion
References
Communicative and Cooperative Learning for Multi-agent Indoor Navigation
1 Introduction
2 Related Work
3 Cooperative Indoor Navigation Task
3.1 Task Definition
3.2 Multi-agent Indoor Navigation Environment
3.3 Data Collection
4 Cooperative Indoor Navigation Models
4.1 Preliminaries
4.2 Framework
5 Experiment
5.1 Benchmarking CIN with MARL Models
5.2 Implementation Details
5.3 Evaluation Metrics
5.4 Quantitative and Qualitative Results
6 Conclusion
References
Enhancing Continuous Domain Adaptation with Multi-path Transfer Curriculum
1 Introduction
2 Methodology
2.1 Preliminary
2.2 Method Framework
2.3 Wasserstein-Based Transfer Curriculum
2.4 Multi-path Optimal Transport
3 Experimental Results
3.1 Datasets and Experimental Configurations
3.2 Analysis of Wasserstein-Based Transfer Curriculum
3.3 Adaptation Comparison Results
3.4 Ablation Study
4 Conclusion
References
Graphs and Networks
Enhancing Network Role Modeling: Introducing Attributed Multiplex Structural Role Embedding for Complex Networks
1 Introduction
2 Related Work
3 Methodology
3.1 Learning Framework
3.2 Model Training
4 Experiments
4.1 Visualization of Attributed Multiplex Role Embeddings
4.2 Node Clustering
4.3 Scalability
5 Studying Influential Roles on Social Media
5.1 Data Collection
5.2 Real-World Multiplex Role Similarity Ranking
5.3 Real-World Multiplex Role Visualization
6 Conclusion
References
Query-Decision Regression Between Shortest Path and Minimum Steiner Tree
1 Introduction
2 Preliminaries
3 A Warm-Up Method: QRTS-P
4 A Probabilistic Perspective: QRTS-D
4.1 Overall Framework
4.2 Hypothesis Design
4.3 QRTS-D
5 Empirical Studies
5.1 Experimental Settings
5.2 Analysis
6 Future Directions
References
Enhancing Policy Gradient for Traveling Salesman Problem with Data Augmented Behavior Cloning
1 Introduction
2 Related Work
3 Behavior Cloning Enhanced Policy Gradient
3.1 Behavior Cloning Pre-training
3.2 Policy Gradient Training
4 Experiments
4.1 Experimental Results
4.2 Ablation Study
5 Conclusion
References
Leveraging Transfer Learning for Enhancing Graph Optimization Problem Solving
1 Introduction
2 Related Work
2.1 Transfer Learning
2.2 Reinforcement Learning for Graph Optimization Problems
3 The State Extraction with Transfer-Learning Framework
3.1 Framework Structure
3.2 Reinforcement Learning Process
4 Experiments
4.1 Effectiveness and Efficiency
4.2 Transferability of Features
5 Conclusion
References
SD-Attack: Targeted Spectral Attacks on Graphs
1 Introduction
2 Preliminaries
2.1 Adversarial Attack on Graphs
2.2 Density of States
3 The Proposed Method SD-Attack
3.1 Spectral Density Distance
3.2 Why Wasserstein Distance
3.3 SD-Attack
4 Experiments
4.1 Setup
4.2 Attack Performance
4.3 Variants of SD-Attack
5 Related Works
6 Conclusion
References
Improving Structural and Semantic Global Knowledge in Graph Contrastive Learning with Distillation
1 Introduction
2 Related Work
3 Methodology
3.1 Distillation Node Contrastive Learning
3.2 Distillation Prototype Contrastive Learning
3.3 Model Learning
4 Experiments
4.1 Experimental Setup
4.2 Experimental Results
5 Conclusion
References
DEGNN: Dual Experts Graph Neural Network Handling both Edge and Node Feature Noise
1 Introduction
2 Related Work
2.1 Graph Neural Networks
2.2 Graph Structure Learning
3 The Proposed Model
3.1 Problem Definition
3.2 Overview
3.3 Node Feature Expert
3.4 Edge Expert
3.5 Downstream Network
3.6 Training Methodology
4 Experiments
4.1 Experimental Setup
4.2 Semi-supervised Node Classification
4.3 Edge Expert Analysis
4.4 Node Feature Expert Analysis
5 Conclusion
References
Alleviating Over-Smoothing via Aggregation over Compact Manifolds
1 Introduction
2 Related Work
3 Preliminaries
4 Contracted Aggregation Problem
4.1 Contracted Aggregation
4.2 Over-Smoothing Due to Contracted Aggregations
4.3 Constructing Non-contracted Aggregations
4.4 Our Non-contracted Aggregation
5 Aggregation over Compact Manifolds
6 Experiments
6.1 Experiments Setup
6.2 Experiment Results
7 Conclusion
References
Are Graph Embeddings the Panacea?
1 Introduction
2 Literature Review
3 Network Characteristics of Datasets
3.1 Dataset Selection
3.2 Summary
4 Experiments, Results and Discussions
4.1 Experiments
4.2 Results and Discussions
5 Conclusion and Future Work
References
Revisiting Link Prediction with the Dowker Complex
1 Introduction
2 Related Work
3 Background
4 Dowker Complex
5 Methodology
6 Experiments
6.1 Results
7 Conclusion
References
GraphNILM: A Graph Neural Network for Energy Disaggregation
1 Introduction
2 Related Work
3 Proposed Model
3.1 Problem Setup: Disaggregation
3.2 GraphNILM
4 Experiment
4.1 Dataset
4.2 Metrics
4.3 Results
5 Conclusion
References
A Contraction Tree SAT Encoding for Computing Twin-Width
1 Introduction
2 Preliminaries
3 Binary SAT Encoding
4 Experiments
5 Conclusion
References
Author Index

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