Neural Information Processing : 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part I / / edited by Teddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto

• Record Nr.: UNINA9910512185403321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
• ISBN: 3-030-92185-9
• Edizione: [1st ed. 2021.]
• Descrizione fisica: 1 online resource (719 pages)
• Collana: Theoretical Computer Science and General Issues, , 2512-2029 ; ; 13108
• Disciplina: 006.32
• Soggetti: Pattern recognition systems; Machine learning; Computer vision; Computer engineering; Computer networks; Education - Data processing; Automated Pattern Recognition; Machine Learning; Computer Vision; Computer Engineering and Networks; Computers and Education
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Theory and Algorithms -- Metric Learning Based Vision Transformer for Product Matching -- Stochastic Recurrent Neural Network for Multistep Time Series Forecasting -- Speaker Verification with Disentangled Self-Attention -- Multi Modal Normalization -- A Focally Discriminative Loss for Unsupervised Domain Adaptation -- Automatic Drum Transcription with Label Augmentation using Convolutional Neural Networks -- Adaptive Curriculum Learning for Semi-Supervised Segmentation of 3D CT-Scans -- Genetic Algorithm and Distinctiveness Pruning in the Shallow Networks for VehicleX -- Stack Multiple Shallow Autoencoders ; into A Strong One: A New Reconstruction-based Method to Detect Anomaly -- Learning Discriminative Representation with Attention and Diversity for Large-scale Face Recognition -- Multi-task Perceptual Occlusion Face Detection with Semantic Attention Network -- RAIDU-Net: Image Inpainting via Residual Attention Fusion and Gated Information Distillation -- Sentence Rewriting with Few-Shot Learningfor Document-Level Event Coreference Resolution -- A Novel Metric Learning Framework for Semi-supervised Domain Adaptation -- Generating Adversarial Examples by Distributed Upsampling -- CPSAM: Channel and Position Squeeze Attention Module -- A Multi-Channel Graph Attention Network for Chinese NER -- GSNESR: A Global Social Network Embedding Approach for Social Recommendation -- Classification Models for Medical Data with Interpretative Rules -- Contrastive Goal Grouping for Policy Generalization in Goal-Conditioned Reinforcement Learning -- Global Fusion Capsule Network with Pairwise-Relation Attention Graph Routing -- MA-GAN: A Method Based on Generative Adversarial Network for Calligraphy Morphing -- One-Stage Open Set Object Detection with Prototype Learning -- Aesthetic-aware Recommender System for Online Fashion Products -- DAFD: Domain Adaptation Framework for Fake News Detection -- Document Image Classification Method based on Graph Convolutional Network -- Continual Learning of 3D Point Cloud Generators -- Attention-Based 3D ResNet for Detection of Alzheimer's Disease Process -- Generation of a Large-Scale Line Image Dataset with Ground Truth Texts from Page-Level Autograph Documents -- DAP-BERT: Differentiable Architecture Pruning of BERT -- Trash Detection On Water Channels -- Tri-Transformer Hawkes Process: Three Heads are better than one -- PhenoDeep: A deep Learning-based approach for detecting reproductive organs from digitized herbarium specimen images -- Document-level Event Factuality Identification using Negation and Speculation Scope -- Dynamic Network Embedding by Time-Relaxed Temporal Random Walk -- Dual-band Maritime Ship Classification based on Multi-layer Convolutional Features and Bayesian Decision -- Context-Based Anomaly Detection via Spatial Attributed Graphs in Human Monitoring -- Domain-Adaptation Person Re-Identification via Style Translation and Clustering -- Multimodal Named Entity Recognition Via Co-attention-based Method with Dynamic Visual Concept Expansion -- Ego Networks -- Cross-modal based Person Re-Identification via Channel Exchange and adversarial Learning -- SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs -- Deep Neuroevolution: Training Neural Networks using a Matrix-free Evolution Strategy -- Weighted P-Rank: A Weighted Article Ranking Algorithm Based on a Heterogeneous Scholarly Network -- Clustering Friendly Dictionary Learning -- Understanding Test-Time Augmentation -- SphereCF: Sphere Embedding for Collaborative Filtering -- Concordant Contrastive Learning for Semi-supervised Node Classification on Graph -- Improving Shallow Neural Networks via Local and Global Normalization -- Underwater Acoustic Target Recognition with Fusion Feature -- Evaluating Data Characterization Measures for Clustering Problems in Meta-learning -- ShallowNet: An Efficient Lightweight Text Detection Network Based on Instance Count-aware Supervision Information -- Image Periodization for Convolutional NeuralNetworks -- BCN-GCN: A Novel Brain Connectivity Network Classification Method via Graph Convolution Neural Network for Alzheimer's Disease -- Triplet Mapping for Continuously Knowledge Distillation -- A Prediction-Augmented AutoEncoder for Multivariate Time Series Anomaly Detection.
• Sommario/riassunto: The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 ; constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic. The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows: Part I: Theory and algorithms; Part II: Theory and algorithms; human centred computing; AI and cybersecurity; Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications; Part IV: Applications.