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

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 proceedin...

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Gorde:
Xehetasun bibliografikoak
Erakunde egilea: SpringerLink (Online service)
Beste egile batzuk: Wand, Michael (Argitaratzailea), Malinovská, Kristína (Argitaratzailea), Schmidhuber, Jürgen (Argitaratzailea), Tetko, Igor V. (Argitaratzailea)
Formatua: Baliabide elektronikoa eBook
Hizkuntza:ingelesa
Argitaratua: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Edizioa:1st ed. 2024.
Saila:Lecture Notes in Computer Science, 15021
Gaiak:
Artificial intelligence.
Computers.
Application software.
Computer networks .
Artificial Intelligence.
Computing Milieux.
Computer and Information Systems Applications.
Computer Communication Networks.
Sarrera elektronikoa:https://doi.org/10.1007/978-3-031-72347-6
Etiketak: Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
Aurkibidea:
  • -- 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.

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