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Neural Networks with Model Compression

Deep learning has achieved impressive results in image classification, computer vision and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floating-point operations (FLOPs)...

Deskribapen osoa

Gorde:
Xehetasun bibliografikoak
Egile Nagusiak: Zhang, Baochang (Egilea), Wang, Tiancheng (Egilea), Xu, Sheng (Egilea), Doermann, David (Egilea)
Erakunde egilea: SpringerLink (Online service)
Formatua: Baliabide elektronikoa eBook
Hizkuntza:ingelesa
Argitaratua: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Edizioa:1st ed. 2024.
Saila:Computational Intelligence Methods and Applications,
Gaiak:
Machine learning.
Artificial intelligence.
Image processing > Digital techniques.
Computer vision.
Machine Learning.
Artificial Intelligence.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer Vision.
Sarrera elektronikoa:https://doi.org/10.1007/978-981-99-5068-3
Etiketak: Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
Aurkibidea:
  • Chapter 1. Introduction
  • Chapter 2. Binary Neural Networks
  • Chapter 3. Binary Neural Architecture Search
  • Chapter 4. Quantization of Neural Networks
  • Chapter 5. Network Pruning
  • Chapter 6. Applications.

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