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  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
Band 15021

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

Aus der Reihe Lecture Notes in Computer Science

73,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.09.2024

Abbildungen

XXXIII, 330 p. 104 illus., 95 illus. in color.

Herausgeber

Michael Wand + weitere

Verlag

Springer

Seitenzahl

330

Maße (L/B/H)

23,5/15,5/2 cm

Gewicht

552 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72346-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.09.2024

Abbildungen

XXXIII, 330 p. 104 illus., 95 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

330

Maße (L/B/H)

23,5/15,5/2 cm

Gewicht

552 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72346-9

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

  • .- Multimodality.


    .- ARIF: An Adaptive Attention-Based Cross-Modal Representation Integration Framework.


    .- BVRCC: Bootstrapping Video Retrieval via Cross-matching Correction.


    .- CAW: Confidence-based Adaptive Weighted Model for Multi-modal Entity Linking.


    .- Cross-Modal Attention Alignment Network with Auxiliary Text Description for zero-shot sketch-based image retrieva.


    .- Exploring Interpretable Semantic Alignment for Multimodal Machine Translation.


    .- Modal fusion-Enhanced two-stream hashing network for Cross modal Retrieval.


    .- Text Visual Question Answering Based on Interactive Learning and Relationship Modeling.


    .- Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment.


    .- Federated Learning.


    .- Addressing the Privacy and Complexity of Urban Traffic  Flow Prediction with Federated Learning and  Spatiotemporal Graph Convolutional Networks.


    .- An Accuracy-Shaping Mechanism for Competitive Distributed Learning.


    .- Federated Adversarial Learning for Robust Autonomous Landing Runway Detection.


    .- FedInc: One-shot Federated Tuning for Collaborative Incident Recognition.


    .- Layer-wised Sparsification Based on Hypernetwork for Distributed NN Training.


    .- Security Assessment of Hierarchical Federated Deep Learning.


    .- Time Series Processing.


    .- ESSformer: Transformers with ESS Attention for Long-Term Series Forecasting.


    .- Fusion of image representations for time series classification with deep learning.


    .- HierNBeats: Hierarchical Neural Basis Expansion Analysis for Hierarchical Time Series Forecasting.


    .- Learning Seasonal-Trend Representations and Conditional Heteroskedasticity for Time Series



    Analysis.


    .- One Process Spatiotemporal Learning of Transformers via Vcls Token for Multivariate Time Series Forecasting.


    .- STformer: Spatio-Temporal Transformer for Multivariate Time Series Anomaly Detection.


    .- TF-CL:Time Series Forcasting Based on Time-Frequency Domain Contrastive Learning.