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Artificial Neural Networks and Machine Learning ICANN 2023: 32nd International [Paperback]

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  • Category: Books (Computers)
  • ISBN-10:  3031442032
  • ISBN-10:  3031442032
  • ISBN-13:  9783031442032
  • ISBN-13:  9783031442032
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Paperback
  • Binding:  Paperback
  • SKU:  3031442032-11-SPRI
  • SKU:  3031442032-11-SPRI
  • Pages:  545
  • Pages:  545
  • Item ID: 106792411
  • List Price: $89.99
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The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 2629, 2023.

The 426 full papers and 9 short papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.


A Comparative Study of Sentence Embedding Models for Assessing Semantic Variation.- A Deep Learning based Method for Generating Holographic Acoustic Fields from Phased Transducer Arrays.- A Depth-guided Attention Strategy for Crowd Counting.- A Noise Convolution Network for Tampering Detection.- Attention-based Feature Interaction Deep Factorization Machine for CTR Prediction.- Block-level Stiffness Analysis of Residual Networks.- CKNA: Kernel Hyperparameters Optimization Method for Group-wise CNNs.- Conditional Convolution Residual Network for Efficient Super-Resolution.- Cross Attention with Deep Local Features for Few-shot Image Classification.- Deep Video Compression Based on 3D Convolution Artifacts Removal and Attention Compression Module.- Deep-learning Based Three Channel Defocused Projection Profilometry.- Depthwise Convolution with Channel Mixer: Rethinking MLP in MetaFormer for Faster and More Accurate Vehicle Detection.- DLUIO: Detecting Useful Investor Opinions by Deep Learning.- Dynamic obstacle avoidance for unmanned aerial vehicle using dynamic vision sensor.- Empirical Study on the Effect of Residual NetworlÓQ
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