Neural information processing : 30th international conference, ICONIP 2023, Changsha, China, November 20-23, 2023, proceedings.

Neural information processing : 30th international conference, ICONIP 2023, Changsha, China, November 20-23, 2023, proceedings. Part XIII / Biao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li, editors.

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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 ICON...

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Bibliographic Details
Online Access: Full Text (via Springer)
Corporate Author: ICONIP (Conference) Changsha, China)
Other Authors: Luo, Biao, Cheng, Long (Computer scientist), Wu, Zheng-Guang, Li, Hongyi, Li, Chaojie
Other title:ICONIP 2023
Format: Conference Proceeding eBook
Language:English
Published: Singapore : Springer Singapore Pte. Limited, 2024.
Series:Communications in computer and information science ; 1967.
Subjects:
Neural computers > Congresses.
Neural networks (Computer science) > Congresses.
Neural computers.
Neural networks (Computer science)
Conference papers and proceedings.
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents - Part XIII
  • Applications
  • Improve Conversational Search with Multi-document Information
  • 1 Introduction
  • 2 Related Works
  • 2.1 Conversational Search
  • 2.2 LLM for Information Retrieval
  • 3 Method
  • 3.1 Preliminaries
  • 3.2 Multi-document Conversation Segmentation
  • 3.3 LLM-Based Document Summarization
  • 3.4 Reranker Based on Passage-Segment-Document Post-training
  • 4 Experiments
  • 4.1 Experimental Settings
  • 4.2 Performance Comparison
  • 4.3 Experimental Results
  • 4.4 Ablation Experiment
  • 5 Conclusion
  • References
  • Recurrent Update Representation Based on Multi-head Attention Mechanism for Joint Entity and Relation Extraction
  • 1 Introduction
  • 2 Related Work
  • 3 Task Formulation
  • 4 Methodology
  • 4.1 Representation Construction
  • 4.2 Multi-head Attention Fusion Layer
  • 4.3 Relation Extraction
  • 5 Experiments
  • 5.1 Experiment Settings
  • 5.2 Experimental Results
  • 5.3 Number of Heads in Multi-head Attention
  • 5.4 Layers of Multi-head Attention Fusion Layers
  • 6 Conclusion
  • References
  • Deep Hashing for Multi-label Image Retrieval with Similarity Matrix Optimization of Hash Centers and Anchor Constraint of Center Pairs
  • 1 Introduction
  • 2 Related Work
  • 3 The Proposed Deep Hashing Framework
  • 3.1 Similarity Matrix Optimization of Hash Centers
  • 3.2 Loss Function of Multi-label Deep Hashing Model
  • 4 Experiments
  • 4.1 Baselines and Datasets
  • 4.2 Experimental Details and Hyperparameter Settings
  • 4.3 Evaluation Criteria
  • 4.4 Experimental Comparison Results
  • 5 Conclusion
  • References
  • MDAM: Multi-Dimensional Attention Module for Anomalous Sound Detection
  • 1 Introduction
  • 2 Related Work
  • 2.1 Attention Mechanism
  • 2.2 Analysis of Temporal and Frequency Dimension Characteristics
  • 3 Method
  • 3.1 Multi-Dimensional Attention Module
  • 3.2 Domain Mixture
  • 4 Experiments
  • 4.1 Experimental Setup
  • 4.2 Comparison of Different Outlier Detection Algorithms
  • 4.3 Pooling Selection
  • 4.4 Comparison with Other Attention Modules
  • 4.5 Effectiveness of Domain Mixture
  • 4.6 Comparison with Other Anomaly Detection Models
  • 5 Conclusion
  • References
  • A Corpus of Quotation Element Annotation for Chinese Novels: Construction, Extraction and Application
  • 1 Introduction
  • 2 Related Work
  • 3 Corpus Construction
  • 3.1 Annotation Schema
  • 3.2 Corpus Analysis
  • 4 Quotation Element Extraction
  • 5 Applications of Quotation Elements
  • 5.1 Character Recognition Based on Quotation Elements
  • 5.2 Gender Classification Based on Quotation Elements
  • 6 Conclusion
  • References
  • Decoupling Style from Contents for Positive Text Reframing
  • 1 Introduction
  • 2 Related Works
  • 2.1 Text Style Transfer
  • 2.2 Pre-trained Language Models
  • 2.3 Contrastive Learning

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