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007			cr |nu|||unuuu
008			240109s2024 si ob 001 0 eng d
010			_a 2023057768
020			_a9789811286711 _q(ebook)
020			_a981128671X _q(ebook)
020			_z9789811286704 _q(hbk.)
040			_aWSPC _beng _cWSPC
050		4	_aQ325.5
072		7	_aCOM _x016000 _2bisacsh
072		7	_aSCI _x089000 _2bisacsh
072		7	_aCOM _x044000 _2bisacsh
082	0	4	_a006.3/1 _223
049			_aMAIN
245	0	0	_aTowards human brain inspired lifelong learning / _ceditors, Xiaoli Li ... [et al.].
260			_aSingapore : _bWorld Scientific, _cc2024.
300			_a1 online resource (xxxv, 238 p.)
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction -- Architectural approaches to continual learning -- Growing RBM on the fly for unsupervised representation toward classification and regression -- Lifelong learning for deep neural networks with Bayesian principles -- Generative replay-based continual zero-shot learning -- Architect, regularize and replay: a flexible hybrid approach for continual learning -- Task-agnostic inference using base-child classifiers -- Flashcards for knowledge capture and replay -- Reliable AI-based decision support system for chest x-ray classification using continual learning.
520			_a"Over the past few decades, the field of machine learning has made remarkable strides, surpassing human performance in tasks like voice and object recognition, as well as mastering various complex games. Despite these accomplishments, a critical challenge remains: the absence of general intelligence. Achieving artificial general intelligence (AGI) requires the development of learning agents that can continually adapt and learn throughout their existence, a concept known as lifelong learning. In contrast to machines, humans possess an extraordinary capacity for continuous learning throughout their lives. Drawing inspiration from human learning, there is immense potential to enable artificial learning agents to learn and adapt continuously. Recent advancements in continual learning research have opened up new avenues to pursue this objective. This book is a comprehensive compilation of diverse methods for continual learning, crafted by leading researchers in the field, along with their practical applications. These methods encompass various approaches, such as adapting existing paradigms like zero-shot learning and Bayesian learning, leveraging the flexibility of network architectures, and employing replay mechanisms to enable learning from streaming data without catastrophic forgetting of previously acquired knowledge. This book is tailored for researchers, practitioners, and PhD scholars working in the realm of Artificial Intelligence (AI). It particularly targets those envisioning the implementation of AI solutions in dynamic environments where data continually shifts, leading to challenges in maintaining model performance for streaming data"-- _cPublisher's website.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
650		0	_aMachine learning.
650		0	_aNeural networks (Computer science)
650		0	_aDeep learning (Machine learning)
655		0	_aElectronic books.
700	1		_aLi, Xiaoli, _d1970-
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/13689#t=toc
942			_cE-BOOK
999			_c49766 _d49766