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Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part III de Lazaros Iliadis

Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part III: Lecture Notes in Computer Science, cartea 14256

Editat de Lazaros Iliadis, Antonios Papaleonidas, Plamen Angelov, Chrisina Jayne
en Limba Engleză Paperback – 22 sep 2023
The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023.
The 426 full papers, 9 short papers and 9 abstract papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.  



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Specificații

ISBN-13: 9783031442124
ISBN-10: 3031442121
Pagini: 593
Ilustrații: XXXV, 593 p. 187 illus., 178 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.87 kg
Ediția:1st ed. 2023
Editura: Springer Nature Switzerland
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Anomaly Detection in Directed Dynamic Graphs via RDGCN and LSTAN.- Anomaly-Based Insider Threat Detection via Hierarchical Information Fusion.- CSEDesc: CyberSecurity Event Detection with Event Description.- GanNeXt: A New Convolutional GAN for Anomaly Detection.- K-Fold Cross-Valuation for Machine Learning Using Shapley Value.- Malicious Domain Detection Based on Self-supervised HGNNs with Contrastive Learning.- Time Series Anomaly Detection with Reconstruction-Based State-Space Models.- ReDualSVG: Refined Scalable Vector Graphics Generation.- Rethinking Feature Context in Learning Image-guided Depth Completion.- Semantic and Frequency Representation Mining for Face Manipulation Detection.- Single image dehazing network based on serial feature attention.- SS-Net: 3D Spatial-Spectral Network for Cerebrovascular Segmentation in TOF-MRA.- STAN: Spatio-Temporal Alignment Network for No-Reference Video Quality Assessment.- Style Expansion without Forgetting for Handwritten Character Recognition.- TransVQ-VAE: Generating Diverse Images using Hierarchical Representation Learning.- UG-Net: Unsupervised-Guided Network for Biomedical Image Segmentation and Classification.- Unsupervised Shape Enhancement and Factorization Machine Network for 3D Face Reconstruction.- Visible-Infrared Person Re-Identification via Modality Augmentation and Center Constraints.- Water Conservancy Remote Sensing Image Classification Based on Target-Scene Deep Semantic Enhancement.- A Partitioned Detection Architecture for Oriented Objects.- A Personalized Federated Multi-Task Learning Scheme for Encrypted Traffic Classification.- Addressing delays in Reinforcement Learning via Delayed Adversarial Imitation Learning.- An Evaluation of Self-Supervised Learning for Portfolio Diversification.- An exploitation-enhanced Bayesian optimization algorithm for high-dimensional expensive problems.- Balancing Selection and Diversity in Ensemble Learning with Exponential Mixture Model.- CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning.- Contrastive Learning and the Emergence of Attributes Associations.- Contrastive Learning for Sleep Staging based on Inter Subject Correlation.- Diffusion Policies as Multi-Agent Reinforcement Learning Strategies.- Dynamic Memory-based Continual Learning with Generating and Screening.- Enhancing Text2SQL Generation with Syntactic Infor-mation and Multi-Task Learning.- Fast Generalizable Novel View Synthesis with Uncertainty-Aware Sampling.- Find Important Training Dataset by Observing the Training Sequence Similarity.- Generating Question-Answer Pairs for Few-shot Learning.- GFedKRL: Graph Federated Knowledge Re-Learning for Effective Molecular Property Prediction via Privacy Protection.- Gradient-Boosted Based Structured and UnstructuredLearning.- Graph Federated Learning Based on the Decentralized Framework.- Heterogeneous Federated Learning Based on Graph Hypernetwork.- Learning to Resolve Conflicts in Multi-Task Learning.- Neighborhood-oriented Decentralized Learning Communication in Multi-Agent System.- NN-Denoising: A Low-Noise Distantly Supervised Document-Level Relation Extraction Scheme using Natural Language Inference and Negative Sampling.- pFedLHNs: Personalized Federated Learning via Local Hypernetworks.- Prototype Contrastive Learning for Personalized Federated Learning.- PTSTEP: Prompt Tuning for Semantic Typing of Event Processes.- SR-IDS: A Novel Network Intrusion Detection System Based on Self-taught Learning and Representation Learning.- Task-Aware Adversarial Feature Perturbation for Cross-Domain Few-Shot Learning.- Ternary Data, Triangle Decoding, Three Tasks, a Multitask Learning Speech Translation Model.