UID:
almahu_9950000759502882
Umfang:
XX, 275 p. 59 illus., 50 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2025.
ISBN:
9783031880360
Serie:
Lecture Notes in Computer Science, 15419
Inhalt:
This book constitutes the refereed proceedings of the 12th International Conference on Analysis of Images, Social Networks and Texts, AIST 2024, held in Bishkek, Kyrgyzstan, during October 17-19, 2024. The 16 full papers included in this book were carefully reviewed and selected from 70 submissions. They were organized in topical sections as follows: Natural Language Processing; Computer Vision; Data Analysis and Machine Learning; and Theoretical Machine Learning and Optimization.
Anmerkung:
-- Keynote and Invited Papers. -- KyrgyzNLP: Challenges, Progress, and Future. -- Modeling Information Influence and Control in Social Networks: Integrating Opinions, Trust, Reputation, and Agent Dynamics. -- Natural Language Processing. -- Graphical Abbreviation Disclosure in Russian Language. -- Iterative Improvement of an Additively Regularized Topic Model. -- Key Algorithms for Keyphrase Generation: Instruction-Based LLMs for Russian Scientific Keyphrases. -- Shrink the longest: improving latent space isotropy with simplicial geometry. -- Redefining Annotation Practices: Leveraging Large Language Models for Discourse Annotation. -- GERA: a corpus of Russian school texts annotated for Grammatical Error Correction. -- From Tokens to Tales: Semantic Similarity in Story Generation. -- Cross-Language Summarization in Russian and Chinese Using the Reinforcement Learning. -- Computer Vision. -- Temporal Modeling via TCN and Transformer for Audio-Visual Emotion Recognition. -- YOLO-HTR: Page-Level Recognition of Historical Handwritten Document Collections. -- Data Analysis and Machine Learning. -- An optimal set of implications in triadic contexts. -- Uniting contrastive and generative learning for event sequences models. -- Theoretical Machine Learning and Optimization. -- An asymptotically optimal algorithm for the minimum weight spanning tree with arbitrarily bounded diameter on random inputs. -- Automatic Adaptive Conformal Inference for Time Series Forecasting.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783031880353
Weitere Ausg.:
Printed edition: ISBN 9783031880377
Sprache:
Englisch
DOI:
10.1007/978-3-031-88036-0
URL:
https://doi.org/10.1007/978-3-031-88036-0
