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008			230330s2023 si | o |||| 0|eng d
020			_a9789819902798
024	7		_a10.1007/978-981-99-0279-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2023 EB
100	1		_aHuang, Yan _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689550
245	1	0	_aDeep Cognitive Networks : _bEnhance Deep Learning by Modeling Human Cognitive Mechanism _cby Yan Huang, Liang Wang
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_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.
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.
988			_aSpringer_Computer_2023
650		7	_2embne _9143900 _aDiseño asistido por ordenador
700	1		_9684958 _aWang, Liang _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-0279-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ek _zSI