Artificial Neural Networks and Machine Learning - ICANN 2024 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part V /
Artificial Neural Networks and Machine Learning - ICANN 2024 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part V / [electronic resource] :
edited by Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko.
- 1st ed. 2024.
- XXXIII, 436 p. 116 illus., 106 illus. in color. online resource.
- Lecture Notes in Computer Science, 15020 1611-3349 ; .
- Lecture Notes in Computer Science, 15020 .
Graph Neural Networks. -- 3D Lattice Deformation Prediction with Hierarchical Graph Attention Networks. -- Beyond Homophily: Attributed Graph Anomaly Detection via Heterophily-aware Contrastive Learning Network. -- Boosting Attributed Graph Anomaly Detection via Negative Sample Awareness. -- CauchyGCN: Preserving Local Smoothness in Graph Convolutional Networks via a Cauchy-Based Message-Passing Scheme and Clustering Analysis. -- ComMGAE: Community Aware Masked Graph AutoEncoder. -- CTQW-GraphSAGE: Trainabel Continuous-Time Quantum Walk On Graph. -- Edged Weisfeiler-Lehman algorithm. -- Enhancing Fraud Detection via GNNs with Synthetic Fraud Node Generation and Integrated Structural Features. -- Graph-Guided Multi-View Text Classification: Advanced Solutions for Fast Inference. -- Invariant Graph Contrastive Learning for Mitigating Neighborhood Bias in Graph Neural Network based Recommender Systems. -- Key Substructure-Driven Backdoor Attacks on Graph Neural Networks. -- Missing Data Imputation via Neighbor Data Feature-enriched Neural Ordinary Differential Equations. -- Multi-graph Fusion and Virtual Node Enhanced Graph Neural Networks. -- STGNA: Spatial-Temporal Graph Convolutional Networks with Node Level Attention for Shortwave Communications Parameters Forecasting. -- Virtual Nodes based Heterogeneous Graph Convolutional Neural Network for Efficient Long-Range Information Aggregation. -- Large Language Models. -- A Three-Phases-LORA Finetuned Hybrid LLM Integrated with Strong Prior Module in the Eduation Context. -- An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration. -- Assessing the Emergent Symbolic Reasoning Abilities of Llama Large Language Models. -- BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks. -- CSAFT: Continuous Semantic Augmentation Fine-Tuning for Legal Large Language Models. -- FashionGPT: A Large Vision-Language Model for Enhancing Fashion Understanding. -- Generative Chain-of-Thought for Zero-shot Cognitive Reasoning. -- Generic Joke Generation with Moral Constraints. -- Large Language Model Ranker with Graph Reasoning for Zero-Shot Recommendation. -- REM: A Ranking-based Automatic Evaluation Method for LLMs. -- Semantics-Preserved Distortion for Personal Privacy Protection in Information Management. -- Towards Minimal Edits in Automated Program Repair: A Hybrid Framework Integrating Graph Neural Networks and Large Language Models. -- Unveiling Vulnerabilities in Large Vision-Language Models: The SAVJ Jailbreak Approach.
The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17-20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.
9783031723445
Artificial intelligence.
Computers.
Application software.
Computer networks .
Artificial Intelligence.
Computing Milieux.
Computer and Information Systems Applications.
Computer Communication Networks.
Q334-342 TA347.A78
006.3
Graph Neural Networks. -- 3D Lattice Deformation Prediction with Hierarchical Graph Attention Networks. -- Beyond Homophily: Attributed Graph Anomaly Detection via Heterophily-aware Contrastive Learning Network. -- Boosting Attributed Graph Anomaly Detection via Negative Sample Awareness. -- CauchyGCN: Preserving Local Smoothness in Graph Convolutional Networks via a Cauchy-Based Message-Passing Scheme and Clustering Analysis. -- ComMGAE: Community Aware Masked Graph AutoEncoder. -- CTQW-GraphSAGE: Trainabel Continuous-Time Quantum Walk On Graph. -- Edged Weisfeiler-Lehman algorithm. -- Enhancing Fraud Detection via GNNs with Synthetic Fraud Node Generation and Integrated Structural Features. -- Graph-Guided Multi-View Text Classification: Advanced Solutions for Fast Inference. -- Invariant Graph Contrastive Learning for Mitigating Neighborhood Bias in Graph Neural Network based Recommender Systems. -- Key Substructure-Driven Backdoor Attacks on Graph Neural Networks. -- Missing Data Imputation via Neighbor Data Feature-enriched Neural Ordinary Differential Equations. -- Multi-graph Fusion and Virtual Node Enhanced Graph Neural Networks. -- STGNA: Spatial-Temporal Graph Convolutional Networks with Node Level Attention for Shortwave Communications Parameters Forecasting. -- Virtual Nodes based Heterogeneous Graph Convolutional Neural Network for Efficient Long-Range Information Aggregation. -- Large Language Models. -- A Three-Phases-LORA Finetuned Hybrid LLM Integrated with Strong Prior Module in the Eduation Context. -- An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaboration. -- Assessing the Emergent Symbolic Reasoning Abilities of Llama Large Language Models. -- BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks. -- CSAFT: Continuous Semantic Augmentation Fine-Tuning for Legal Large Language Models. -- FashionGPT: A Large Vision-Language Model for Enhancing Fashion Understanding. -- Generative Chain-of-Thought for Zero-shot Cognitive Reasoning. -- Generic Joke Generation with Moral Constraints. -- Large Language Model Ranker with Graph Reasoning for Zero-Shot Recommendation. -- REM: A Ranking-based Automatic Evaluation Method for LLMs. -- Semantics-Preserved Distortion for Personal Privacy Protection in Information Management. -- Towards Minimal Edits in Automated Program Repair: A Hybrid Framework Integrating Graph Neural Networks and Large Language Models. -- Unveiling Vulnerabilities in Large Vision-Language Models: The SAVJ Jailbreak Approach.
The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17-20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.
9783031723445
Artificial intelligence.
Computers.
Application software.
Computer networks .
Artificial Intelligence.
Computing Milieux.
Computer and Information Systems Applications.
Computer Communication Networks.
Q334-342 TA347.A78
006.3
