Artificial Neural Networks and Machine Learning – ICANN 2024: 33rd International Conference on Artif… - SLUB Dresden

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

Beteiligte: Wand, Michael [Herausgeber:in]; Malinovská, Kristína [Herausgeber:in]; Schmidhuber, Jürgen [Herausgeber:in]; Tetko, Igor V. [Herausgeber:in]

Erschienen: Cham: Springer Nature Switzerland, 2024.
Cham: Imprint: Springer, 2024.

Ausgabe: 1st ed. 2024.

Umfang: 1 Online-Ressource(XXXIV, 495 p. 155 illus., 143 illus. in color.)

Sprache: Englisch

DOI: 10.1007/978-3-031-72356-8

ISBN: 9783031723568

Identifikator:

Schlagwörter: Artificial intelligence. ; Computers. ; Application software. ; Computer networks .

Entstehung:

Anmerkungen:

Beschreibung: -- Human-Computer Interfaces. -- Combining Contrastive Learning and Sequence Learning for Automated Essay Scoring. -- PIDM: Personality-aware Interaction Diffusion Model for gesture generation. -- Prompt Design using Past Dialogue Summarization for LLMs to Generate the Current Appropriate Dialogue. -- Recommender Systems. -- Click-Through Rate Prediction Based on Filtering-enhanced with Multi-Head Attention. -- Enhancing Sequential Recommendation via Aligning Interest Distributions. -- LGCRS: LLM-Guided Representation-Enhancing for Conversational Recommender System. -- Multi-intent Aware Contrastive Learning for Sequential Recommendation. -- Subgraph Collaborative Graph Contrastive Learning for Recommendation. -- Time-Aware Squeeze-Excitation Transformer for Sequential Recommendation. -- Environment and Climate. -- Carbon Price Forecasting with LLM-based Refinement and Transfer-Learning. -- Challenges, Methods, Data – a Survey of Machine Learning in Water Distribution Networks. -- Day-ahead scenario analysis of wind power based on ICGAN and IDTW-Kmedoids. -- Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models. -- Hybrid CNN-MLP for Wastewater Quality Estimation. -- Short-term Forecasting of Wind Power Using CEEMDAN-ICOA-GRU Model. -- City Planning. -- Predicting City Origin-Destination Flow with Generative Pre-training. -- Vehicle-based Evolutionary Travel Time Estimation with Deep Meta Learning. -- Machine Learning in Engineering and Industry. -- APF-DQN: Adaptive Objective Pathfinding via Improved Deep Reinforcement Learning among Building Fire Hazard. -- DDPM-MoCo: Enhancing the Generation and Detection of Industrial Surface Defects through Generative and Contrastive Learning. -- Detecting Railway Track Irregularities Using Conformal Prediction. -- Identifying the Trends of Technological Convergence between Domains using a Heterogeneous Graph Perspective: A Case Study of the Graphene Industry. -- Machine Learning Accelerated Prediction of 3D Granular Flows in Hoppers. -- RD-Crack: A Study of Concrete Crack Detection Guided by a Residual Neural Network Improved Based on Diffusion Modeling. -- Applications in Finance. -- Anomaly Detection in Blockchain Using Multi-source Embedding and Attention Mechanism. -- Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems. -- MSIF: Multi-Source Information Fusion for Financial Question Answering. -- Artificial Intelligence in Education. -- A Temporal-Enhanced Model for Knowledge Tracing. -- Social Network Analysis. -- Position and type aware anchor link prediction across social networks. -- Artificial Intelligence and Music. -- LSTM-MorA: Melody-Accompaniment Classification of MIDI Tracks. -- Software Security. -- Ch4os: Discretized Generative Adversarial Network for Functionality-preserving Evasive Modification on Malware. -- SSA-GAT: Graph-based Self-supervised Learning for Network Intrusion Detection.

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.

Artificial Neural Networks and Machine Learning – ICANN 2024 - [1st ed. 2024.]

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