Artificial Neural Networks and Machine Learning – ICANN 2024
Springer International Publishing (Verlag)
978-3-031-72355-1 (ISBN)
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.
.- 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.
| Erscheinungsdatum | 18.09.2024 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | XXXIV, 495 p. 155 illus., 143 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | Artificial Intelligence • classification • Deep learning • generative models • graph neural networks • Image Processing • Large Language Models • machine learning • Neural networks • Reinforcement Learning • reservoir computing • Robotics • spiking neural networks |
| ISBN-10 | 3-031-72355-4 / 3031723554 |
| ISBN-13 | 978-3-031-72355-1 / 9783031723551 |
| Zustand | Neuware |
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