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001453226 001__ 1453226
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001453226 019__ $$a1350687879
001453226 020__ $$a9783031168680$$q(electronic bk.)
001453226 020__ $$a3031168682$$q(electronic bk.)
001453226 020__ $$z9783031168673
001453226 020__ $$z3031168674
001453226 0247_ $$a10.1007/978-3-031-16868-0$$2doi
001453226 035__ $$aSP(OCoLC)1350617419
001453226 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF$$dOCLCQ
001453226 049__ $$aISEA
001453226 050_4 $$aQ342
001453226 08204 $$a006/.3$$223/eng/20221116
001453226 1001_ $$aSun, Yanan,$$eauthor.
001453226 24510 $$aEvolutionary deep neural architecture search :$$bfundamentals, methods, and recent advances /$$cYanan Sun, Gary G. Yen, Mengjie Zhang.
001453226 264_1 $$aCham :$$bSpringer,$$c[2023]
001453226 264_4 $$c©2023
001453226 300__ $$a1 online resource (xvi, 331 pages) :$$billustrations (chiefly color).
001453226 336__ $$atext$$btxt$$2rdacontent
001453226 337__ $$acomputer$$bc$$2rdamedia
001453226 338__ $$aonline resource$$bcr$$2rdacarrier
001453226 4901_ $$aStudies in computational intelligence ;$$vvolume 1070
001453226 504__ $$aIncludes bibliographical references.
001453226 5050_ $$aIntro -- Preface -- References -- Contents -- Acronyms -- Part I Fundamentals and Backgrounds -- 1 Evolutionary Computation -- 1.1 Genetic Algorithms (GAs) -- 1.2 Particle Swarm Optimization (PSO) -- 1.3 Differential Evolution (DE) -- 1.4 Genetic Programming (GP) -- 1.5 Chapter Summary -- References -- 2 Deep Neural Networks -- 2.1 Deep Belief Networks -- 2.2 Stacked Auto-Encoders -- 2.2.1 Sparse Auto-Encoders -- 2.2.2 Weight Decay Auto-Encoders -- 2.2.3 Denoising Auto-Encoders (DAEs) -- 2.2.4 Contractive Auto-Encoders -- 2.2.5 Convolutional Auto-Encoders (CAEs)
001453226 5058_ $$a2.2.6 Variational Auto-Encoders (VAEs) -- 2.3 Convolutional Neural Networks (CNNs) -- 2.3.1 CNN Skeleton -- 2.3.2 Convolution -- 2.3.3 Pooling -- 2.3.4 Reflect Padding -- 2.3.5 Batch Normalization (BN) -- 2.3.6 ResNet Blocks (RBs) and DenseNet Blocks (DBs) -- 2.4 Benchmarks for Deep Neural Networks -- 2.5 Chapter Summary -- References -- Part II Evolutionary Deep Neural Architecture Search for Unsupervised DNNs -- 3 Architecture Design for Stacked AEs and DBNs -- 3.1 Introduction -- 3.2 Related Work and Motivations -- 3.2.1 Unsupervised Deep Learning
001453226 5058_ $$a3.2.2 Evolutionary Algorithms for Evolving Neural Networks -- 3.3 Algorithm Details -- 3.3.1 Framework of EUDNN -- 3.3.2 Evolving Connection Weights and Activation Functions -- 3.3.3 Fine-Tuning Connection Weights -- 3.3.4 Discussion -- 3.4 Experimental Design -- 3.4.1 Performance Metric -- 3.4.2 Peer Competitors -- 3.4.3 Parameter Settings -- 3.5 Experimental Results and Analysis -- 3.5.1 Performance of EUDNN -- 3.5.2 Analysis on Pre-training of EUDNN -- 3.5.3 Analysis on Fine-Tuning of EUDNN -- 3.5.4 Representation Visualizations -- 3.6 Chapter Summary -- References
001453226 5058_ $$a4 Architecture Design for Convolutional Auto-Encoders -- 4.1 Introduction -- 4.2 Motivation of FCAE -- 4.3 Algorithm Details -- 4.3.1 Algorithm Overview -- 4.3.2 Encoding Strategy -- 4.3.3 Particle Initialization -- 4.3.4 Fitness Evaluation -- 4.3.5 Velocity Calculation and Position Update -- 4.3.6 Deep Training on Global Best Particle -- 4.4 Experimental Design -- 4.4.1 Peer Competitors -- 4.4.2 Parameter Settings -- 4.5 Experimental Results and Analysis -- 4.5.1 Overview Performance -- 4.5.2 Evolution Trajectory of PSOAO -- 4.5.3 Performance on Different Numbers of Training Examples
001453226 5058_ $$a4.5.4 Investigation on x-Reference Velocity Calculation -- 4.6 Chapter Summary -- References -- 5 Architecture Design for Variational Auto-Encoders -- 5.1 Introduction -- 5.2 Algorithm Details -- 5.2.1 Algorithm Overview -- 5.2.2 Strategy of Gene Encoding -- 5.2.3 Initialization of Population -- 5.2.4 Evaluation -- 5.2.5 Crossover Operator and Mutation Operator -- 5.2.6 Environmental Selection -- 5.3 Experimental Design -- 5.3.1 Parameter Setting -- 5.3.2 Peer Competitors -- 5.3.3 Performance Evaluation -- 5.4 Experimental Results and Analysis -- 5.4.1 Overall Performance
001453226 506__ $$aAccess limited to authorized users.
001453226 520__ $$aThis book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.
001453226 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 16, 2022).
001453226 650_0 $$aComputational intelligence.
001453226 650_0 $$aArtificial intelligence.
001453226 655_0 $$aElectronic books.
001453226 7001_ $$aYen, Gary G.,$$eauthor.
001453226 7001_ $$aZhang, Mengjie,$$eauthor.
001453226 77608 $$iPrint version: $$z3031168674$$z9783031168673$$w(OCoLC)1342105774
001453226 830_0 $$aStudies in computational intelligence ;$$vv. 1070.
001453226 852__ $$bebk
001453226 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-16868-0$$zOnline Access$$91397441.1
001453226 909CO $$ooai:library.usi.edu:1453226$$pGLOBAL_SET
001453226 980__ $$aBIB
001453226 980__ $$aEBOOK
001453226 982__ $$aEbook
001453226 983__ $$aOnline
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