• Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
Band 15024

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

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.09.2024

Abbildungen

XXXIV, 495 p. 155 illus., 143 illus. in color.

Herausgeber

Michael Wand + weitere

Verlag

Springer

Seitenzahl

495

Maße (L/B/H)

23,5/15,5/2,9 cm

Gewicht

797 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72355-1

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.09.2024

Abbildungen

XXXIV, 495 p. 155 illus., 143 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

495

Maße (L/B/H)

23,5/15,5/2,9 cm

Gewicht

797 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72355-1

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024

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    .- 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.


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    .- 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.


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    .- Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models.


    .- Hybrid CNN-MLP for Wastewater Quality Estimation.


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    .- Vehicle-based Evolutionary Travel Time Estimation with Deep Meta Learning.


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    .- APF-DQN: Adaptive Objective Pathfinding via Improved Deep Reinforcement Learning among



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    .- DDPM-MoCo: Enhancing the Generation and Detection of Industrial Surface Defects through



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    .- 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.


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