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Artificial Neural Networks and Machine Learning - ICANN 2021

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The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th Internati... Weiterlesen
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Beschreibung

The 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 generative neural networks, graph neural networks, hierarchical and ensemble models, human pose estimation, image processing, image segmentation, knowledge distillation, and medical image processing.

*The conference was held online 2021 due to the COVID-19 pandemic.




Inhalt

Generative neural networks.- Binding and Perspective Taking as Inference in a Generative Neural Network Model.- Advances in Password Recovery using Generative Deep Learning Techniques.- o 0886 - Dilated Residual Aggregation Network for Text-guided Image Manipulation.- Denoising AutoEncoder based Delete and Generate Approach for Text Style Transfer.- GUIS2Code: A Computer Vision Tool to Generate Code Automatically from Graphical User Interface Sketches.- Generating Math Word Problems from Equations with Topic Consistency Maintaining and Commonsense Enforcement.- Generative properties of Universal Bidirectional Activation-based Learning.- Graph neural networks I.- Joint Graph Contextualized Network for Sequential Recommendation.- Relevance-Aware Q-matrix Calibration for Knowledge Tracing.- LGACN: A Light Graph Adaptive Convolution Network for Collaborative Filtering.- HawkEye: Cross-Platform Malware Detection with Representation Learning on Graphs.- An Empirical Study of the Expressiveness of Graph Kernels and Graph Neural Networks.- Multi-resolution Graph Neural Networks for PDE approximation.- Link Prediction on Knowledge Graph by Rotation Embedding on the Hyperplane in the Complex Vector Space.- Graph neural networks II.- Contextualise Entities and Relations: An Interaction Method for Knowledge Graph Completion.- Civil Unrest Event Forecasting Using Graphical and Sequential Neural Networks.- Parameterized Hypercomplex Graph Neural Networks for Graph Classification.- Feature Interaction Based Graph Convolutional Networks For Image-text Retrieval.- Generalizing Message Passing Neural Networks to Heterophily using Position Information.- Local and Non-local Context Graph Convolutional Networks for Skeleton-based Action Recognition.-STGATP: A Spatio-temporal Graph Attention Network for Long-term Traffic Prediction.- Hierarchical and ensemble models.- Integrating N-Gram Features into Pre-Trained Model: A Novel Ensemble Model for Multi-Target Stance Detection.- Hierarchical Ensemble for Multi-view Clustering.- Structure-Aware Multi-Scale Hierarchical Graph Convolutional Network for Skeleton Action Recognition.- Learning Hierarchical Reasoning for Text-based Visual Question Answering.- Hierarchical Deep Gaussian Processes Latent Variable Model via Expectation Propagation.- Adaptive Consensus-Based Ensemble for Improved Deep Learning Inference Cost.- Human pose estimation.- Multi-Branch Network for Small Human Pose Estimation.- PNO: Personalized Network Optimization for Human Pose and Shape Reconstruction.- JointPose: Jointly Optimizing Evolutionary Data Augmentation and Prediction Neural Network for 3D Human Pose Estimation.- DeepRehab: Real Time Pose Estimation on the Edge for Knee Injury Rehabilitation.- Image processing.- Subspace constraint for Single Image Super-Resolution.- Towards Fine-Grained Control over Latent Space for Unpaired Image-to-Image Translation.- FMSNet: Underwater Image Restoration by Learning from a Synthesized Dataset.- Towards Measuring Bias in Image Classification.- Towards Image Retrieval with Noisy Labels via Non-deterministic Features.- Image segmentation.- Improving Visual Question Answering by Semantic Segmentation.- Weakly Supervised Semantic Segmentation with Patch-Based Metric Learning Enhancement.- ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation.- Depth Mapping Hybrid Deep Learning Method for Optic Disc and Cup Segmentation on Stereoscopic Ocular Fundus.- RATS: Robust Automated Tracking and Segmentation of Similar Instances.- Knowledge distillation.- Data Diversification Revisited: Why Does It Work?.- A Generalized Meta-Loss Function for Distillation Based Learning Using Privileged Information for Classification and Regression.- Empirical Study of Data-Free Iterative Knowledge Distillation.- Adversarial Variational Knowledge Distillation.- Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation.- Medical image processing.- Semi-supervised Learning based Right Ventricle Segmentation Using Deep Convolutional Boltzmann Machine Shape Model.- Improved U-Net for Plaque Segmentation of Intracoronary Optical Coherence Tomography Images.- Approximated Masked Global Context Network for Skin Lesion Segmentation.- DSNet: Dynamic Selection Network for Biomedical Image Segmentation.- Computational Approach to Identifying Contrast-Driven Retinal Ganglion Cells.- Radiological Identification of Hip Joint Centers from X-ray Images Using Fast Deep Stacked Network and Dynamic Registration Graph.- A Two-Branch Neural Network for Non-Small-Cell Lung Cancer Classification and Segmentation.- Uncertainty Quantification and Estimation in Medical Image Classification.- Labeling Chest X-Ray Reports Using Deep Learning.

Produktinformationen

Titel: Artificial Neural Networks and Machine Learning - ICANN 2021
Untertitel: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part III
Editor:
EAN: 9783030863647
ISBN: 3030863646
Format: Kartonierter Einband
Herausgeber: Springer International Publishing
Anzahl Seiten: 724
Gewicht: 1077g
Größe: H235mm x B155mm x T38mm
Jahr: 2021
Untertitel: Englisch
Auflage: 1st ed. 2021

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