Full Description
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.
Contents
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.
