Neural Information Processing : 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VI / edited by Biao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li.

Format:

Book

Contributor:

Luo, Biao, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 14452

Language:

English

Subjects (All):

Pattern recognition systems.

Data mining.

Machine learning.

Social sciences--Data processing.

Social sciences.

Automated Pattern Recognition.

Data Mining and Knowledge Discovery.

Machine Learning.

Computer Application in Social and Behavioral Sciences.

Local Subjects:

Automated Pattern Recognition.

Data Mining and Knowledge Discovery.

Machine Learning.

Computer Application in Social and Behavioral Sciences.

Physical Description:

1 online resource (521 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

The six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. .

Contents:

MIC: An Effective Defense Against Word-level Textual Backdoor Attacks

Active Learning for Open-set Annotation Using Contrastive Query Strategy

Cross-Domain Bearing Fault Diagnosis Method Using Hierarchical Pseudo Labels

Differentiable Topics Guided New Paper Recommendation

IIHT: Medical Report Generation with Image-to-Indicator Hierarchical Transformer

OD-Enhanced Dynamic Spatial-Temporal Graph Convolutional Network for Metro Passenger Flow Prediction

Enhancing Heterogeneous Graph Contrastive Learning with Strongly Correlated Subgraphs

DRPDDet: Dynamic Rotated Proposals Decoder for Oriented object detection

MFSFFuse: Multi-Receptive Field Feature Extraction for Infrared and Visible Image Fusion using Self-Supervised Learning

Progressive Temporal Transformer for Bird's-Eye-View Camera Pose Estimation

Adaptive Focal Inverse Distance Transform Maps for Cell Recognition

Stereo Visual Mesh for Generating Sparse Semantic Maps at High Frame Rates

Micro-Expression Recognition Based on PCB-PCANet+

Exploring Adaptive Regression Loss and Feature Focusing in Industrial Scenarios

Optimal Task Grouping Approach in Multitask Learning

Effective Guidance in Zero-Shot Multilingual Translation via Multiple Language Prototypes

Extending DenseHMM with Continuous Emission

An Efficient Enhanced-YOLOv5 Algorithm for Multi-scale Ship Detection

Double-Layer Blockchain-Based Decentralized Integrity Verification for Multi-Chain Cross-Chain Data

Inter-modal Fusion Network with Graph Structure Preserving for Fake News Detection

Learning to Match Features with Geometry-aware Pooling

PnP: Integrated Prediction and Planning for Interactive Lane Change in Dense Traffic

Towards Analyzing the Efficacy of Multi-task Learning in Hate Speech Detection

Exploring Non-Isometric Alignment Inference for Representation Learning of Irregular Sequences

Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain

Improving GNSS-R Sea Surface Wind Speed Retrieval from FY-3E Satellite Using Multi-Task Learning and Physical Information

Incorporating Syntactic Cognitive in Multi-granularity Data Augmentation for Chinese Grammatical Error Correction

Long Short-Term Planning for Conversational Recommendation Systems

Gated Bi-View Graph Structure Learning

How Legal Knowledge Graph Can Help Predict Charges for Legal Text

CMFN: Cross-Modal Fusion Network for Irregular Scene Text Recognition

Introducing Semantic-based Receptive Field into Semantic Segmentation via Graph Neural Networks

Transductive Cross-Lingual Scene-Text Visual Question Answering

Learning Representations for Sparse Crowd Answers

Identify Vulnerability Types: A Cross-Project Multiclass Vulnerability Classification System based on Deep Domain Adaptation.

Other Format:

Print version: Luo, Biao Neural Information Processing

ISBN:

981-9980-76-3