Neural Networks with Model Compression (Record no. 87459)
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fixed length control field | 04153nam a22006015i 4500 |
001 - CONTROL NUMBER | |
control field | 978-981-99-5068-3 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240730171227.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 240205s2024 si | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9789819950683 |
-- | 978-981-99-5068-3 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 006.31 |
100 1# - AUTHOR NAME | |
Author | Zhang, Baochang. |
245 10 - TITLE STATEMENT | |
Title | Neural Networks with Model Compression |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2024. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | IX, 260 p. 101 illus., 67 illus. in color. |
490 1# - SERIES STATEMENT | |
Series statement | Computational Intelligence Methods and Applications, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | 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. |
520 ## - SUMMARY, ETC. | |
Summary, etc | 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) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, our book will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS due to its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge about machine learning and deep learning to better understand the methods described in this book. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
General subdivision | Digital techniques. |
700 1# - AUTHOR 2 | |
Author 2 | Wang, Tiancheng. |
700 1# - AUTHOR 2 | |
Author 2 | Xu, Sheng. |
700 1# - AUTHOR 2 | |
Author 2 | Doermann, David. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1007/978-981-99-5068-3 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Singapore : |
-- | Springer Nature Singapore : |
-- | Imprint: Springer, |
-- | 2024. |
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-- | computer |
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-- | online resource |
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-- | text file |
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Machine learning. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Artificial intelligence. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Image processing |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer vision. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Machine Learning. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Artificial Intelligence. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Imaging, Vision, Pattern Recognition and Graphics. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Vision. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 2510-1773 |
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