Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part XIII

The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023.   The 1274 papers presented in the proceedings set were carefully reviewed and selected from 652 submissions.  The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.

Table of Contents:
​Applications.- Improve Conversational Search with Multi-Document Information.- Recurrent Update Representation Based on Multi-Head Attention Mechanism for Joint Entity and Relation Extraction.- p Hashing for Multi-label Image Retrieval with Similarity Matrix Optimization of Hash Centers and Anchor Constraint of Center Pairs.- MDAM: Multi-Dimensional Attention Module for Anomalous Sound Detection.- A Corpus of Quotation Element Annotation for Chinese Novels: Construction, Extraction and Application.- Decoupling Style from Contents for Positive Text Reframing.- Multi-level Feature Enhancement Method For Medical Text Detection.- Neuron Attribution-Based Attacks Fooling Object Detectors.- DKCS: A Dual Knowledge-Enhanced Abstractive Cross-Lingual Summarization Method based on Graph Attention Networks.- A Joint Identification Network for Legal Event Detection.- YOLO-D: Dual-branch infrared distant target detection based on multi-levelweighted feature fusion.- Graph Convolutional Network based Feature Constraints Learning for Cross-Domain Adaptive Recommendation.- A Hybrid Approach Using Convolution and Transformer for Mongolian Ancient Documents Recognition.- Incomplete Multi-view Subspace Clustering Using Non-Uniform Hyper-Graph for High-Order Information.- Deep Learning-Empowered Unsupervised Maritime Anomaly Detection.- Hazardous Driving Scenario Identification with Limited Training Samples.- Machine Unlearning with Affine Hyperplane Shifting and Maintaining for Image Classification.- An Interpretable Vulnerability Detection Method Based on Multi-task Learning.- Co-GAN:A Text-to-Image Synthesis Model with Local and Integral Features.- Graph Contrastive ATtention Network for Rumor Detection.- E3-MG:End-to-End Expert Linking via Multi-Granularity Representation Learning.- TransCenter: Transformer in Heatmap and A New Form of Bounding Box.- Causal-Inspired Influence Maximization in Hypergraphs Under Temporal Constraints.- Enhanced Generation of Human Mobility Trajectory with Multiscale Model.- SRLI:Handling Irregular Time Series with a Novel Self-supervised Model based on Contrastive Learning.- Multimodal Event Classification in Social Media.- ADV-POST: Physically Realistic Adversarial Poster for Attacking Semantic Segmentation Models in Autonomous Driving.- Uformer++: Light Uformer for Image Restoration.- Can language really understand depth?.- Remaining Useful Life Prediction of Control Moment Gyro in Orbiting Spacecraft based on Variational Autoencoder.- Dynamic Feature Distillation.- Detection of Anomalies and Explanation in Cybersecurity.- Document-Level Relation Extraction with Relation Correlation Enhancement.- Multi-scale Directed Graph Convolution Neural Network for Node Classification Task.- Dual Knowledge Distillation for Neural Machine Translation.- Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting.- Stereoential Net: Deep Network for Learning Building Height Using Stereo Imagery.- FEGI: A Fusion Extractive-Generative Model for Dialogue Ellipsis and Coreference Integrated Resolution.- Assessing and Enhancing LLMs: A Physics and History Dataset and One-More-Check Pipeline Method.- Sub-Instruction and Local Map Relationship Enhanced Model for Vision and Language Navigation.- TFormer: Cross-Level Feature Fusion in Object Detection.- Improving Handwritten Mathematical Expression Recognition via an Attention Refinement Network.- Dual-Domain Learning For JPEG Artifacts Removal.- Graph-based Vehicle Keypoint Attention Model for Vehicle Re-identification.- POI Recommendation based on Double-level Spatio-temporal Relationship in Locations and Categories.- Multi-Feature Integration Neural Network with Two-Stage Training for Short-Term Load Forecasting.