001461982 000__ 03948cam\a22005657a\4500
001461982 001__ 1461982
001461982 003__ OCoLC
001461982 005__ 20230503003420.0
001461982 006__ m\\\\\o\\d\\\\\\\\
001461982 007__ cr\un\nnnunnun
001461982 008__ 230403s2023\\\\si\\\\\\ob\\\\000\0\eng\d
001461982 020__ $$a9789819902798$$q(electronic bk.)
001461982 020__ $$a9819902797$$q(electronic bk.)
001461982 020__ $$z9819902789
001461982 020__ $$z9789819902781
001461982 0247_ $$a10.1007/978-981-99-0279-8$$2doi
001461982 035__ $$aSP(OCoLC)1374521597
001461982 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF
001461982 049__ $$aISEA
001461982 050_4 $$aQ325.73
001461982 08204 $$a006.3/1$$223/eng/20230407
001461982 1001_ $$aHuang, Yan.
001461982 24510 $$aDeep cognitive networks :$$benhance deep learning by modeling human cognitive mechanism /$$cYan Huang, Liang Wang.
001461982 260__ $$aSingapore :$$bSpringer,$$c2023.
001461982 300__ $$a1 online resource.
001461982 4901_ $$aSpringerBriefs in computer science
001461982 504__ $$aIncludes bibliographical references.
001461982 5050_ $$aChapter 1. Introduction -- Chapter 2. General Framework -- Chapter 3. Attention-based DCNs -- Chapter 4. Memory-based DCNs -- Chapter 5. Reasoning-based DCNs -- Chapter 6. Decision-based DCNs -- Chapter 7. Conclusions and Future Trends.
001461982 506__ $$aAccess limited to authorized users.
001461982 520__ $$aAlthough deep learning models have achieved great progress in vision, speech, language, planning, control, and many other areas, there still exists a large performance gap between deep learning models and the human cognitive system. Many researchers argue that one of the major reasons accounting for the performance gap is that deep learning models and the human cognitive system process visual information in very different ways. To mimic the performance gap, since 2014, there has been a trend to model various cognitive mechanisms from cognitive neuroscience, e.g., attention, memory, reasoning, and decision, based on deep learning models. This book unifies these new kinds of deep learning models and calls them deep cognitive networks, which model various human cognitive mechanisms based on deep learning models. As a result, various cognitive functions are implemented, e.g., selective extraction, knowledge reuse, and problem solving, for more effective information processing. This book first summarizes existing evidence of human cognitive mechanism modeling from cognitive psychology and proposes a general framework of deep cognitive networks that jointly considers multiple cognitive mechanisms. Then, it analyzes related works and focuses primarily but not exclusively, on the taxonomy of four key cognitive mechanisms (i.e., attention, memory, reasoning, and decision) surrounding deep cognitive networks. Finally, this book studies two representative cases of applying deep cognitive networks to the task of image-text matching and discusses important future directions.
001461982 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 7, 2023).
001461982 650_0 $$aDeep learning (Machine learning)
001461982 655_0 $$aElectronic books.
001461982 7001_ $$aWang, Liang.
001461982 77608 $$iPrint version: $$z9819902789$$z9789819902781$$w(OCoLC)1363103518
001461982 830_0 $$aSpringerBriefs in computer science.
001461982 852__ $$bebk
001461982 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-0279-8$$zOnline Access$$91397441.1
001461982 909CO $$ooai:library.usi.edu:1461982$$pGLOBAL_SET
001461982 980__ $$aBIB
001461982 980__ $$aEBOOK
001461982 982__ $$aEbook
001461982 983__ $$aOnline
001461982 994__ $$a92$$bISE