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Deep Learning Theory and Applications: Third International Conference, DeLTA 2022, Lisbon, Portugal, July 1214, 2022, Revised Selected Papers 1st ed. 2023 [Pehme köide]

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  • Formaat: Paperback / softback, 121 pages, kõrgus x laius: 235x155 mm, kaal: 215 g, 47 Illustrations, color; 2 Illustrations, black and white; IX, 121 p. 49 illus., 47 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 1858
  • Ilmumisaeg: 07-Jul-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031373162
  • ISBN-13: 9783031373169
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  • Formaat: Paperback / softback, 121 pages, kõrgus x laius: 235x155 mm, kaal: 215 g, 47 Illustrations, color; 2 Illustrations, black and white; IX, 121 p. 49 illus., 47 illus. in color., 1 Paperback / softback
  • Sari: Communications in Computer and Information Science 1858
  • Ilmumisaeg: 07-Jul-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031373162
  • ISBN-13: 9783031373169
This book constitutes the refereed post-conference proceedings of the Third International Conference on Deep Learning Theory and Applications, DeLTA 2022, held in Lisbon, Portugal, during January 17-18, 2022.

The 6 full papers included in this book were carefully reviewed and selected from 36 submissions. They present recent research on machine learning and artificial intelligence in real-world applications such as computer vision, information retrieval and summarization from structured and unstructured multimodal data sources, natural language understanding and translation, and many other application domains.
Modified SkipGram Negative Sampling Model for Faster Convergence of
Graph Embedding.- Active Collection of Well-being and Health Data in Mobile
Devices.- Reliable Classification of Images by Calculating Their Credibility
using a Layer-wise Activation Cluster Analysis of CNNs.- Trac Sign
Repositories: Bridging the Gap between Real and
Synthetic Data.- Convolutional Neural Networks for Structural Damage
Localization on Digital Twins.- Evaluating and Improving RoSELS for Road
Surface Extraction from 3D Automotive LiDAR Point Cloud Sequences.