Binary neural networks : (Record no. 56176)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 08375nam a2200673 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | on1406407261 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | EBZ |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20251029164110.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu---unuuu |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 231029s2024 flu ob 001 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | EBZ |
| Language of cataloging | eng |
| Transcribing agency | EBZ |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781003816799 |
| Qualifying information | electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1003816797 |
| Qualifying information | electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781003376132 |
| Qualifying information | electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1003376134 |
| Qualifying information | electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781003816850 |
| Qualifying information | electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1003816851 |
| Qualifying information | electronic book |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781032452487 |
| Qualifying information | hardcover |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781032452500 |
| Qualifying information | paperback |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1201/9781003376132 |
| Source of number or code | doi |
| 037 ## - SOURCE OF ACQUISITION | |
| Stock number | 9781003376132 |
| Source of stock number/acquisition | Taylor & Francis |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.87 |
| Item number | .Z43 2023 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 044000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 030000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 043000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UYQN |
| Source | bicssc |
| 049 ## - LOCAL HOLDINGS (OCLC) | |
| Holding library | MAIN |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Zhang, Baochang. |
| -- | https://id.oclc.org/worldcat/entity/E39PCjFvwYXtgx3fhPCh4P6h73 |
| 9 (RLIN) | 65306 |
| 245 10 - TITLE STATEMENT | |
| Title | Binary neural networks : |
| Remainder of title | algorithms, architectures, and applications. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | First edition. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Boca Raton, FL : |
| Name of producer, publisher, distributor, manufacturer | CRC Press, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (xii, 205 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 341 0# - | |
| -- | textual |
| -- | sapdv |
| -- | EBSCOhost |
| 490 1# - SERIES STATEMENT | |
| Series statement | Multimedia Computing, Communication and Intelligence Series |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 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 floatingpoint 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, Binary Neural Networks: Algorithms, Architectures, and Applications 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 and binary NAS and 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 of machine learning and deep learning to better understand the methods described in this book. Key Features Reviews recent advances in CNN compression and acceleration Elaborates recent advances on binary neural network (BNN) technologies Introduces applications of BNN in image classification, speech recognition, object detection, and more |
| 532 #0 - | |
| -- | EBSCOhost |
| -- | "EBSCO evaluates our products based on the Web Content Accessibility Guidelines (WCAG) and the related Section 508 and EN 301 549 regulations in the US and EU. Most EBSCO products are substantially conformant with WCAG 2.2 level AA." Source: https://connect.ebsco.com/s/article/EBSCO-VPATs?language=en_US. Last accessed April 22, 2025. |
| 545 0# - BIOGRAPHICAL OR HISTORICAL DATA | |
| Biographical or historical data | Baochang Zhang is a full Professor with Institute of Artificial Intelligence, Beihang University, Beijing, China. He was selected by the Program for New Century Excellent Talents in University of Ministry of Education of China, also selected as Academic Advisor of Deep Learning Lab of Baidu Inc., and a distinguished researcher of Beihang Hangzhou Institute in Zhejiang Province. His research interests include explainable deep learning, computer vision and patter recognition. His HGPP and LDP methods were state-of-the-art feature descriptors, with 1234 and 768 Google Scholar citations, respectively. Both are "Test-of-Time" works. Our 1-bit methods achieved the best performance on ImageNet. His group also won the ECCV 2020 tiny object detection, COCO object detection, and ICPR 2020 Pollen recognition challenges. Sheng Xu received the B.E. degree in Automotive Engineering from Beihang University, Beijing, China. He is currently a Ph.D. with the school of Automation Science and Electrical Engineering, Beihang University, Beijing, China, specializing in computer vision, model quantization, and compression. He has made significant contributions to the field and has published about a dozen papers as the first author in top-tier conferences and journals such as CVPR, ECCV, NeurIPS, AAAI, BMVC, IJCV, and ACM TOMM. Notably, he has 4 papers selected as oral or highlighted presentations by these prestigious conferences. Furthermore, Sheng Xu actively participates in the academic community as a reviewer for various international journals and conferences, including CVPR, ICCV, ECCV, NeurIPS, ICML, and IEEE TCSVT. His expertise has also led to his group's victory in the ECCV 2020 tiny object detection challenge. Mingbao Lin finished his M.S.-Ph.D. study and obtained the Ph.D. degree in intelligence science and technology from Xiamen University, Xiamen, China, in 2022. Earlier, he received the B.S. degree from Fuzhou University, Fuzhou, China, in 2016. He is currently a senior researcher with the Tencent Youtu Lab, Shanghai, China. His publications on top-tier conferences/journals include IEEE TPAMI, IJCV, IEEE TIP, IEEE TNNLS, CVPR, NeurIPS, AAAI, IJCAI, ACM MM and so on. His current research interest is to develop efficient vision model, as well as information retrieval. Tiancheng Wang received the B.E. degree in Automation from Beihang University, Beijing, China. He is currently pursuing the Ph.D. degree with the school of Institute of Artificial Intelligence, Beihang University, Beijing, China. During undergraduate, he has been awarded the title of Merit Student for several consecutive years, and has received various scholarships including academic excellence scholarship and academic competitions scholarship, etc. He was involved in several AI projects, including behavior detection and intention understanding research and unmanned air-based vision platform, etc. Now, his current research interests include deep learning and network compression, his goal is to explore the highly energy-saving model and drive the deployment of neural networks in embedded devices. Dr. David Doermann is a Professor of Empire Innovation at the University at Buffalo (UB) and the Director of the University at Buffalo Artificial Intelligence Institute. Prior to coming to UB, he was a program manager at the Defense Advanced Research Projects Agency (DARPA), where he developed, selected and oversaw approximately $150 million in research and transition funding in the areas of computer vision, human language technologies and voice analytics. He coordinated performers on all of the projects, orchestrating consensus, evaluating cross team management and overseeing fluid program objectives. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| 9 (RLIN) | 64231 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| Source of heading or term | bisacsh |
| 9 (RLIN) | 64989 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| Source of heading or term | bisacsh |
| 9 (RLIN) | 65307 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | |
| Source of heading or term | bisacsh |
| 9 (RLIN) | 65308 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Xu, Sheng. |
| 9 (RLIN) | 65309 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Lin, Mingbao. |
| 9 (RLIN) | 65310 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Wang, Tiancheng. |
| 9 (RLIN) | 65311 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Doermann, David. |
| 9 (RLIN) | 65312 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Multimedia computing, communication and intelligence. |
| 9 (RLIN) | 65313 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | EBSCOhost |
| Uniform Resource Identifier | <a href="https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3710433">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3710433</a> |
| 938 ## - | |
| -- | Askews and Holts Library Services |
| -- | ASKH |
| -- | AH41557300 |
| 938 ## - | |
| -- | YBP Library Services |
| -- | YANK |
| -- | 20542857 |
| 938 ## - | |
| -- | EBSCOhost |
| -- | EBSC |
| -- | 3710433 |
| 994 ## - | |
| -- | 92 |
| -- | N$T |
No items available.