PRICAI 2024: Trends in Artificial Intelligence
Springer Nature Switzerland AG (Verlag)
978-981-96-0118-9 (ISBN)
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The five-volume proceedings set LNAI 15281-15285, constitutes the refereed proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, held in Kyoto, Japan, in November 18-24, 2024.
The 145 full papers and 35 short papers included in this book were carefully reviewed and selected from 543 submissions.
The papers are organized in the following topical sections:
Part I: Machine Learning, Deep Learning
Part II: Deep Learning, Federated Learning, Generative AI, Natural Language Processing, Large Language Models,
Part III: Large Language Models, Computer Vision
Part IV: Computer Vision, Autonomous Driving, Agents and Multiagent Systems, Knowledge Graphs, Speech Processing, Optimization
Part V: Optimization, General Applications, Medical Applications, Theoretical Foundations of AI
.- Deep Learning.
.- STLB-GN: Spatio-Temporal Dual Graph Network with Learnable Bases.
.- Rethinking the Reliability of Post-hoc Calibration Methods under Subpopulation Shift.
.- Zero-shot Heterogeneous Graph Embedding via Semantic Extraction.
.- TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data.
.- Stock Market Index Movement Prediction using Partial Contextual Embedding BERT-LSTM.
.- SCBC: A Supervised Single-cell Classification Method Based on Batch Correction for ATAC-seq Data.
.- TS-CATMA: A Lung Cancer Electronic Nose Data Classification Method Based on Adversarial Training and Multi-Scale Attention.
.- Visualizing the Unseen: Arabic Image-to-Story Generation Using Deep Learning Techniques.
.- Federated Learning.
.- Federated Prompt Tuning: When is it Necessary?.
.- Dirichlet-Based Local Inconsistency Query Strategy for Active Domain Adaptation.
.- FedSD: Cross-Heterogeneous Federated Learning Based on Self-Distillation.
.- Personalized Federated Learning with Feature Alignment via Knowledge Distillation.
.- Multi-Party Collaborative Hate Speech Study on Social Media via Personalized Federated Learning.
.- Preserving Individual User's Right to be Forgotten in Enterprise-Level Federated Learning.
.- Generative AI.
.- Dance Generation From Music with Enhanced Beat.
.- Contrastive Prototype Network for Generative Zero-Shot learning.
.- Steganography: An improved robust model for deep hidden network.
.- Human- and AI-Generated Marketing Content Comparison Corpus, Evaluation, and Detection.
.- Natural Language Processing.
.- Mongolian-Chinese Cross-lingual Topic Detection Based on Knowledge Distillation and Contrastive Learning Methods.
.- Emergence of Grounded Language Representations for Continuous Object Properties through Decentralized Embodied Learning.
.- AI-facilitation for consensus-building by virtual discussion using large language models.
.- False Positive Detection for Text-based Person Retrieval.
.- An End-to-End Method for Chinese Spelling Error Detection and Correction.
.- Dialogue Summarization based on Feature Extraction and Commonsense Injection.
.- SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq .- Personalized Generation with Causal Inference.
.- Document-Level Relation Extraction Model Based On Boundary Distance Loss And Long-Tail Relation Enhancement.
.- MCQG: Reading Comprehension Multiple Choice Questions Generation based on Pre-trained Language Models.
.- ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification.
.- EC-PEFT: An Expertise-Centric Parameter-Efficient Fine-Tuning Framework for Large Language Models.
.- Enhanced Classification of Delay Risk Sources in Road Construction Using Domain- Knowledge-Driven.
.- Modeling the Structural and Semantic Features for Japanese Lyrics Generation of J-pop Songs.
.- FINE-LMT: Fine-grained Feature Learning for Multi-Modal Machine Translation.
.- Segmentation Strategies and Data Enrichment for Improved Abstractive Summarization of Burmese Language.
.- Constrained Reasoning Chains for Enhancing Theory-of-Mind in Large Language Models.
.- Spatial-Temporal Union Channel Enhancement for Continuous Sign Language Recognition.
.- KLoB: a Benchmark for Assessing Knowledge Localization Methods in Language Models.
.- Cross-lingual Entity Alignment Model based on Multi-entity Enhancement and Semantic Information.
.- Large Language Models.
.- A Decomposed-Distilled Sequential Framework for Text-to-Table Task with LLMs.
.- Are Dense Retrieval Models Few-Shot Learners?.
.- An Empirical Study of Leveraging PLMs and LLMs for Long-Text Summarization.
.- A Novel MLLMs-based Two-stage Model for Zero-shot Multimodal Sentiment Analysis.
.- DeepTTS: Enhanced Transformer-Based Text Spotter via Deep Interaction Between Detection and Recognition Tasks.
| Erscheinungsdatum | 19.11.2024 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | 126 Illustrations, color; 17 Illustrations, black and white |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Agents • Artificial Intelligence • Bioinformatics • computer vision • Decision Theory • Deep learning • generative AI • Large Language Models • machine learning • Natural Language Processing • Optimization |
| ISBN-10 | 981-96-0118-5 / 9819601185 |
| ISBN-13 | 978-981-96-0118-9 / 9789819601189 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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