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001439644 019__ $$a1268573843
001439644 020__ $$a9783030863401$$q(electronic bk.)
001439644 020__ $$a3030863409$$q(electronic bk.)
001439644 020__ $$z9783030863395$$q(print)
001439644 0247_ $$a10.1007/978-3-030-86340-1$$2doi
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001439644 049__ $$aISEA
001439644 050_4 $$aQA76.87
001439644 08204 $$a006.32$$223
001439644 1112_ $$aInternational Conference on Artificial Neural Networks (European Neural Network Society)$$n(30th :$$d2021 :$$cOnline)
001439644 24510 $$aArtificial neural networks and machine learning -- ICANN 2021 :$$b30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings.$$nPart II /$$cIgor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter (eds.).
001439644 2463_ $$aICANN 2021
001439644 264_1 $$aCham, Switzerland :$$bSpringer,$$c2021.
001439644 300__ $$a1 online resource (xxiii, 651 pages) :$$billustrations (some color)
001439644 336__ $$atext$$btxt$$2rdacontent
001439644 337__ $$acomputer$$bc$$2rdamedia
001439644 338__ $$aonline resource$$bcr$$2rdacarrier
001439644 347__ $$atext file
001439644 347__ $$bPDF
001439644 4901_ $$aLecture notes in computer science ;$$v12892
001439644 4901_ $$aLNCS sublibrary, SL 1, Theoretical computer science and general issues
001439644 500__ $$a"This year, due to the still unresolved pandemic, the Organizing Committee, together with the Executive Committee of ENNS decided to organize ICANN 2021 online ..."--Preface
001439644 500__ $$aIncludes author index.
001439644 5050_ $$aComputer vision and object detection -- Selective Multi-Scale Learning for Object Detection -- DRENet: Giving Full Scope to Detection and Regression-based Estimation for Video Crowd Counting -- Sisfrutos Papaya: a Dataset for Detection and Classification of Diseases in Papaya -- Faster-LTN: a neuro-symbolic, end-to-end object detection architecture -- GC-MRNet: Gated Cascade Multi-stage Regression Network for Crowd Counting -- Latent Feature-Aware and Local Structure-Preserving Network for 3D Completion from a single depth view -- Facial Expression Recognition by Expression-Specific Representation Swapping -- Iterative Error Removal for Time-of-Flight Depth Imaging -- Blurred Image Recognition: A Joint Motion Deblurring and Classification Loss-Aware Approach -- Learning How to Zoom in: Weakly Supervised ROI-based-DAM for Fine-Grained Visual Classification -- Convolutional neural networks and kernel methods -- (Input) Size Matters for CNN Classifiers -- Accelerating Depthwise Separable Convolutions with Vector Processor -- KCNet: Kernel-based Canonicalization Network for entities in Recruitment Domain -- Deep Unitary Convolutional Neural Networks -- Deep learning and optimization I -- DPWTE: A Deep Learning Approach to Survival Analysis using a Parsimonious Mixture of Weibull Distributions -- First-order and second-order variants of the gradient descent in a unified framework -- Bayesian optimization for backpropagation in Monte-Carlo tree search -- Growing Neural Networks Achieve Flatter Minima -- Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks -- Curved SDE-Net Leads to Better Generalization for Uncertainty Estimates of DNNs -- EIS -- Efficient and Trainable Activation Functions for Better Accuracy and Performance -- Deep learning and optimization II -- Why Mixup Improves the Model Performance -- Mixup gamblers: Learning to abstain with auto-calibrated reward for mixed samples -- Non-Iterative Phase Retrieval With Cascaded Neural Networks -- Incorporating Discrete Wavelet Transformation Decomposition Convolution into Deep Network to Achieve Light Training -- MMF: A loss extension for feature learning in open set recognition -- On the selection of loss functions under known weak label models -- Distributed and continual learning -- Bilevel Online Deep Learning in Non-stationary Environment -- A Blockchain Based Decentralized Gradient Aggregation Design for Federated Learning -- Continual Learning for Fake News Detection from Social Media -- Balanced Softmax Cross-Entropy for Incremental Learning -- Generalised Controller Design using Continual Learning -- DRILL: Dynamic Representations for Imbalanced Lifelong Learning -- Principal Gradient Direction and Confidence Reservoir Sampling for Continual Learning -- Explainable methods -- Spontaneous Symmetry Breaking in Data Visualization -- Deep NLP Explainer: Using Prediction Slope To Explain NLP Models -- Empirically explaining SGD from a line search perspective -- Towards Ontologically Explainable Classifiers -- Few-shot learning -- Leveraging the Feature Distribution in Transfer-based Few-Shot Learning -- One-Shot Meta-Learning for Radar-Based Gesture Sequences Recognition -- Few-Shot Learning With Random Erasing and Task-Relevant Feature Transforming -- Fostering Compositionality in Latent, Generative Encodings to Solve the Omniglot Challenge -- Better Few-shot Text Classification with Pre-trained Language Model -- Generative adversarial networks -- Leveraging GANs via Non-local Features -- On Mode Collapse in Generative Adversarial Networks -- Image Inpainting Using Wasserstein Generative Adversarial Imputation Network -- COViT-GAN: Vision Transformer for COVID-19 Detection in CT Scan Images with Self-Attention GAN for Data Augmentation -- PhonicsGAN: Synthesizing Graphical Videos from Phonics Songs -- A Progressive Image Inpainting Algorithm with a Mask Auto-update Branch -- Hybrid Generative Models for Two-Dimensional Datasets -- Towards Compressing Efficient Generative Adversarial Networks for Image Translation via Pruning and Distilling.
001439644 506__ $$aAccess limited to authorized users.
001439644 520__ $$aThe proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as computer vision and object detection, convolutional neural networks and kernel methods, deep learning and optimization, distributed and continual learning, explainable methods, few-shot learning and generative adversarial networks. *The conference was held online 2021 due to the COVID-19 pandemic.
001439644 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 16, 2021).
001439644 650_0 $$aNeural networks (Computer science)$$vCongresses.
001439644 650_0 $$aMachine learning$$vCongresses.
001439644 650_0 $$aArtificial intelligence$$vCongresses.
001439644 650_6 $$aRéseaux neuronaux (Informatique)$$vCongrès.
001439644 650_6 $$aApprentissage automatique$$vCongrès.
001439644 650_6 $$aIntelligence artificielle$$vCongrès.
001439644 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001439644 655_7 $$aConference papers and proceedings.$$2lcgft
001439644 655_7 $$aActes de congrès.$$2rvmgf
001439644 655_0 $$aElectronic books.
001439644 7001_ $$aFarkaš, Igor,$$eeditor.$$0(orcid)0000-0003-3503-2080$$1https://orcid.org/0000-0003-3503-2080
001439644 7001_ $$aMasulli, Paolo,$$eeditor$$1https://orcid.org/0000-0002-1389-3894
001439644 7001_ $$aOtte, Sebastian,$$eeditor$$0(orcid)0000-0002-0305-0463$$1https://orcid.org/0000-0002-0305-0463
001439644 7001_ $$aWermter, Stefan,$$eeditor$$1https://orcid.org/0000-0003-1343-4775
001439644 77608 $$iPrint version: $$z9783030863395
001439644 77608 $$iPrint version: $$z9783030863418
001439644 830_0 $$aLecture notes in computer science ;$$v12892.
001439644 830_0 $$aLNCS sublibrary.$$nSL 1,$$pTheoretical computer science and general issues.
001439644 852__ $$bebk
001439644 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-86340-1$$zOnline Access$$91397441.1
001439644 909CO $$ooai:library.usi.edu:1439644$$pGLOBAL_SET
001439644 980__ $$aBIB
001439644 980__ $$aEBOOK
001439644 982__ $$aEbook
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