Artificial neural networks and machine learning - ICANN 2024 Part 5
.- 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..- CauchyGC...
Gespeichert in:
| Körperschaft: | |
|---|---|
| Weitere Verfasser: | , , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Cham
Springer
2024
|
| Schriftenreihe: | Lecture notes in computer science
15020 |
| Schlagworte: |
Angewandte Informatik
> COM094000
> COMPUTERS / Computer Science
> COMPUTERS / Computer Vision & Pattern Recognition
> COMPUTERS / Hardware / Network Hardware
> COMPUTERS / Neural Networks
> Computer vision
> Information technology: general issues
> Machine learning
> Maschinelles Lernen
> Maschinelles Sehen, Bildverstehen
> Network hardware
> Netzwerk-Hardware
> Neural networks & fuzzy systems
> Neuronale Netze und Fuzzysysteme
> Konferenzschrift
|
| Online Zugang: | Cover |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Tag hinzufügen 
| Zusammenfassung: | .- 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 |
|---|---|
| Beschreibung: | Literaturangaben |
| Beschreibung: | xxxiii, 436 Seiten Diagramme |
| ISBN: | 9783031723438 978-3-031-72343-8 |