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Titlebook: PRICAI 2024: Trends in Artificial Intelligence; 21st Pacific Rim Int Rafik Hadfi,Patricia Anthony,Quan Bai Conference proceedings 2025 The

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發(fā)表于 2025-3-21 18:05:21 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱PRICAI 2024: Trends in Artificial Intelligence
副標(biāo)題21st Pacific Rim Int
編輯Rafik Hadfi,Patricia Anthony,Quan Bai
視頻videohttp://file.papertrans.cn/765/764626/764626.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: PRICAI 2024: Trends in Artificial Intelligence; 21st Pacific Rim Int Rafik Hadfi,Patricia Anthony,Quan Bai Conference proceedings 2025 The
描述.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.
出版日期Conference proceedings 2025
關(guān)鍵詞Artificial Intelligence; Machine Learning; Deep Learning; Agents; Decision Theory; Large Language Models;
版次1
doihttps://doi.org/10.1007/978-981-96-0122-6
isbn_softcover978-981-96-0121-9
isbn_ebook978-981-96-0122-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 20:34:46 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:48:04 | 只看該作者
Conference proceedings 2025puter 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.
地板
發(fā)表于 2025-3-22 05:42:00 | 只看該作者
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發(fā)表于 2025-3-22 09:00:13 | 只看該作者
MLRQA: A Dataset with?Multimodal Logical Reasoning Challengesto encourage the reasoning potential of multimodal large language models (MLLMs). Experimental demonstrate that state-of-the-art GPT-4v performs significantly worse than the human benchmark. Our dataset can also serve as an important benchmark for investigating multimodal logical reasoning in future. The dataset is available at ..
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發(fā)表于 2025-3-22 13:59:48 | 只看該作者
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發(fā)表于 2025-3-22 19:03:02 | 只看該作者
Predicting Plain Text Imageability for?Faithful Prompt-Conditional Image Generationalso developed a new visual-text dataset, named Ted1.6k, to facilitate model training and validation. Experiment results demonstrate the effectiveness of the proposed method in promoting prompt-guided image generation.
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發(fā)表于 2025-3-22 22:51:41 | 只看該作者
0302-9743 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
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發(fā)表于 2025-3-23 04:10:50 | 只看該作者
MLRQA: A Dataset with?Multimodal Logical Reasoning Challengesully understanding the content. Multimodal logical reasoning represents a significant research area within logical reasoning and recent methods focus on exploring the capacity of large language models to tackle this task. However, existing multimodal logic reasoning datasets lack rigorous manual val
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發(fā)表于 2025-3-23 07:24:34 | 只看該作者
Fame Bias – Large Language Models Change Their Judgement Depending on?Personal Name analysis tool. It extracts examples from NLI and story cloze task datasets with personal names, replaces them with names of celebrities, ordinary people or mixtures. Results show that altered names significantly influence model assessments, especially with more than two choices, indicating potentia
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