Overview

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.

Full Product Details

Author:   Michael Wand ,  Kristína Malinovská ,  Jürgen Schmidhuber ,  Igor V. Tetko
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2024 ed.
Volume:   15020
ISBN:  

9783031723438

ISBN 10:   3031723430
Pages:   436
Publication Date:   17 September 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

.- 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.

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