Vor Ort
Merkzettel Warenkorb
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
Band 15020

Artificial Neural Networks and Machine Learning – ICANN 2024 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part V

Aus der Reihe Lecture Notes in Computer Science

78,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.09.2024

Abbildungen

XXXIII, 436 p. 116 illus., 106 illus. in color.

Herausgeber

Michael Wand + weitere

Verlag

Springer

Seitenzahl

436

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

709 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72343-8

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.09.2024

Abbildungen

XXXIII, 436 p. 116 illus., 106 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

436

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

709 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72343-8

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024


  • .- 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.