LEADER 03968nam 22006135 450 001 9910629296103321 005 20251113183355.0 010 $a9783031168680 010 $a3031168682 024 7 $a10.1007/978-3-031-16868-0 035 $a(MiAaPQ)EBC7133758 035 $a(Au-PeEL)EBL7133758 035 $a(CKB)25299467800041 035 $a(OCoLC)1350687879 035 $a(PPN)266349102 035 $a(DE-He213)978-3-031-16868-0 035 $a(EXLCZ)9925299467800041 100 $a20221108d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEvolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances /$fby Yanan Sun, Gary G. Yen, Mengjie Zhang 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (335 pages) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v1070 311 08$a9783031168673 311 08$a3031168674 320 $aIncludes bibliographical references. 327 $aPart I: Fundamentals and Backgrounds -- Evolutionary Computation -- Deep Neural Networks -- Part II: Evolutionary Deep Neural Architecture Search for Unsupervised DNNs -- Architecture Design for Stacked AEs and DBNs -- Architecture Design for Convolutional Auto-Encoders -- Architecture Design for Variational Auto-Encoders -- Part III: Evolutionary Deep Neural Architecture Search for Supervised DNNs -- Architecture Design for Plain CNNs -- Architecture Design for RBs and DBs Based CNNs -- Architecture Design for Skip-Connection Based CNNs -- Hybrid GA and PSO for Architecture Design -- Internet Protocol Based Architecture Design -- Di?erential Evolution for Architecture Design -- Architecture Design for Analyzing Hyperspectral Images -- Part IV: Recent Advances in Evolutionary Deep Neural Architecture Search -- Encoding Space Based on Directed Acyclic Graphs -- End-to-End Performance Predictors -- Deep Neural Architecture Pruning -- Deep Neural Architecture Compression -- Distribution Training Framework for Architecture Design. 330 $aThis book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v1070 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a060 676 $a006.3 700 $aSun$b Yanan$01265805 702 $aYen$b Gary G. 702 $aZhang$b Mengjie 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910629296103321 996 $aEvolutionary Deep Neural Architecture Search$92968249 997 $aUNINA