LEADER 07264nam 22006615 450 001 9910887879403321 005 20251225194932.0 010 $a3-031-72332-5 024 7 $a10.1007/978-3-031-72332-2 035 $a(MiAaPQ)EBC31680968 035 $a(Au-PeEL)EBL31680968 035 $a(CKB)35895640900041 035 $a(DE-He213)978-3-031-72332-2 035 $a(OCoLC)1457083404 035 $a(EXLCZ)9935895640900041 100 $a20240917d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Neural Networks and Machine Learning ? ICANN 2024 $e33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17?20, 2024, Proceedings, Part I /$fedited by Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (497 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15016 311 08$a3-031-72331-7 327 $a -- Theory of Neural Networks and Machine Learning. -- Multi-label Robust Feature Selection via Subspace-Sparsity Learning. -- Nullspace-based metric for classification of dynamical systems and sensors. -- On the Bayesian Interpretation of Robust Regression Neural Networks. -- Probability-Generating Function Kernels for Spherical Data. -- Tailored Finite Point Operator Networks for Interface problems. -- Novel Methods in Machine Learning. -- A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class. -- Adaptive Compression of the Latent Space in Variational Autoencoders. -- Asymmetric Isomap for Dimensionality Reduction and Data Visualization. -- CALICO: Confident Active Learning with Integrated Calibration. -- Improved Multi-hop Reasoning through Sampling and Aggregating. -- Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks. -- Revealing Unintentional Information Leakage in Low-Dimensional Facial Portrait Representations. -- Safe Data Resampling Method based on Counterfactuals Analysis. -- Test-Time Augmentation for Traveling Salesperson Problem. -- Novel Neural Architectures. -- Resonator-Gated RNNs. -- Towards a model of associative memory with learned distributed representations. -- Neural Architecture Search. -- Accelerated NAS via pretrained ensembles and multi-fidelity Bayesian Optimization. -- Feature Activation-Driven Zero-Shot NAS: A Contrastive Learning Framework. -- NAS-Bench-Compre: A Comprehensive Neural Architecture Search Benchmark with Customizable Components. -- NAVIGATOR-D3: Neural Architecture search using VarIational Graph Auto-encoder Toward Optimal aRchitecture Design for Diverse Datasets. -- ResBuilder: Automated Learning of Depth with Residual Structures -- Self-Organization. -- A Neuron Coverage-based Self-Organizing Approach for RBFNNs in Multi-Class Classification Tasks. -- Self-Organising Neural Discrete Representation Learning ŕ la Kohonen. -- Neural Processes. -- Combined Global and Local Information Diffusion of Neural Processes. -- Topology of Neural Processes. -- Novel Architectures for Computer Vision. -- DEEPAM: Toward Deeper Attention Module in Residual Convolutional Neural Networks. -- Differentiable Largest Connected Component Layer for Image Mattin. -- Enhancing Generalization in Convolutional Neural Networks through Regularization with Edge and Line Features. -- Transformer Tracker based on Multi-level Residual Perception Structure. .-Multimodal Architectures. -- CAW: Confidence-based Adaptive Weighted Model for Multi-modal Entity Linking. -- Exploring Interpretable Semantic Alignment for Multimodal Machine Translation. -- Fairness in Machine Learning. -- CFP: A Reinforcement Learning Framework for Comprehensive Fairness-Performance Trade-off in Machine Learning. 330 $aThe ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17?20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15016 606 $aArtificial intelligence 606 $aComputers 606 $aApplication software 606 $aComputer networks 606 $aArtificial Intelligence 606 $aComputing Milieux 606 $aComputer and Information Systems Applications 606 $aComputer Communication Networks 615 0$aArtificial intelligence. 615 0$aComputers. 615 0$aApplication software. 615 0$aComputer networks. 615 14$aArtificial Intelligence. 615 24$aComputing Milieux. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Communication Networks. 676 $a006.3 700 $aWand$b Michael$01323454 701 $aMalinovská$b Kristína$01769610 701 $aSchmidhuber$b Ju?rgen$00 701 $aTetko$b Igor V$01769612 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910887879403321 996 $aArtificial Neural Networks and Machine Learning ? ICANN 2024$94241388 997 $aUNINA LEADER 03590nam 2200721Ia 450 001 9910963059703321 005 20251117083627.0 010 $a9786613600882 010 $a9781280571282 010 $a1280571284 010 $a9780300175226 010 $a0300175221 024 7 $a10.12987/9780300183566 035 $a(CKB)2670000000184281 035 $a(OCoLC)794489367 035 $a(CaPaEBR)ebrary10551236 035 $a(SSID)ssj0000835016 035 $a(PQKBManifestationID)11458140 035 $a(PQKBTitleCode)TC0000835016 035 $a(PQKBWorkID)10989816 035 $a(PQKB)11229707 035 $a(DE-B1597)486406 035 $a(DE-B1597)9780300183566 035 $a(Au-PeEL)EBL3420838 035 $a(CaPaEBR)ebr10551236 035 $a(CaONFJC)MIL360088 035 $a(OCoLC)923598106 035 $a(MiAaPQ)EBC3420838 035 $a(Perlego)1089872 035 $z(OCoLC)794489367 035 $a(EXLCZ)992670000000184281 100 $a20111114d2012 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe brain $ebig bangs, behaviors, and beliefs /$fRob DeSalle and Ian Tattersall ; illustrated by Patricia J. Wynne 205 $a1st ed. 210 $aNew Haven $cYale University Press$dc2012 215 $a1 online resource (369 p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9780300183566 311 08$a0300183569 320 $aIncludes bibliographical references and index. 327 $tFrontmatter -- $tContents -- $tPreface -- $tAcknowledgments -- $t1. The Nature Of Science: Our Brains At Work -- $t2. The Nitty-Gritty Of The Nervous System -- $t3. Hanging Our Brains On The Tree Of Life -- $t4. Making Sense Of Senses -- $t5. Processing Information -- $t6. Emotions And Memory -- $t7. Brain EvoDevo -- $t8. Words And Music By . . . -- $t9. Decisions, Behaviors, And Beliefs -- $t10. The Human Brain And Cognitive Evolution -- $tEpilogue -- $tTimeline -- $tGlossary -- $tLiterature Cited And Further Reading -- $tIndex 330 $aAfter several million years of jostling for ecological space, only one survivor from a host of hominid species remains standing: us. Human beings are extraordinary creatures, and it is the unprecedented human brain that makes them so. In this delightfully accessible book, the authors present the first full, step-by-step account of the evolution of the brain and nervous system.Tapping the very latest findings in evolutionary biology, neuroscience, and molecular biology, Rob DeSalle and Ian Tattersall explain how the cognitive gulf that separates us from all other living creatures could have occurred. They discuss the development and uniqueness of human consciousness, how human and nonhuman brains work, the roles of different nerve cells, the importance of memory and language in brain functions, and much more. Our brains, they conclude, are the product of a lengthy and supremely untidy history-an evolutionary process of many zigs and zags-that has accidentally resulted in a splendidly eccentric and creative product. 606 $aCognition 606 $aNeurophysiology 606 $aBrain$xEvolution 615 0$aCognition. 615 0$aNeurophysiology. 615 0$aBrain$xEvolution. 676 $a612.8/2 700 $aDeSalle$b Rob$0546347 701 $aTattersall$b Ian$0451652 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910963059703321 996 $aThe brain$94365831 997 $aUNINA