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Artificial Neural Networks and Machine Learning ¿ ICANN 2022

af Plamen Angelov, Chrisina Jayne, Mehmet Aydin, Elias Pimenidis, Antonios Papaleonidas

indgår i Lecture Notes in Computer Science serien

Artificial Neural Networks and Machine Learning ¿ ICANN 2022 af Plamen Angelov
Bag om Artificial Neural Networks and Machine Learning ¿ ICANN 2022

The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications. Chapters ¿Learning Flexible Translation Between Robot Actions and Language Descriptions¿, ¿Learning Visually Grounded Human-Robot Dialog in a Hybrid Neural Architecture¿ are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031159305
  • Indbinding:
  • Paperback
  • Sideantal:
  • 840
  • Udgivet:
  • 7. september 2022
  • Udgave:
  • 22001
  • Størrelse:
  • 155x45x235 mm.
  • Vægt:
  • 1247 g.
  • 8-11 hverdage.
  • 17. januar 2025
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Beskrivelse af Artificial Neural Networks and Machine Learning ¿ ICANN 2022

The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022.
The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications. Chapters ¿Learning Flexible Translation Between Robot Actions and Language Descriptions¿, ¿Learning Visually Grounded Human-Robot Dialog in a Hybrid Neural Architecture¿ are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

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