標題: Titlebook: PRICAI 2024: Trends in Artificial Intelligence; 21st Pacific Rim Int Rafik Hadfi,Patricia Anthony,Quan Bai Conference proceedings 2025 The [打印本頁] 作者: Malicious 時間: 2025-3-21 18:18
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書目名稱PRICAI 2024: Trends in Artificial Intelligence網(wǎng)絡公開度學科排名
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書目名稱PRICAI 2024: Trends in Artificial Intelligence被引頻次學科排名
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書目名稱PRICAI 2024: Trends in Artificial Intelligence讀者反饋
書目名稱PRICAI 2024: Trends in Artificial Intelligence讀者反饋學科排名
作者: ALTER 時間: 2025-3-21 23:30 作者: 注意力集中 時間: 2025-3-22 00:53 作者: maladorit 時間: 2025-3-22 04:36
Zakaria Elabid,Lena Sasal,Daniel Busby,Abdenour Hadidint thinking’ that focuses on the formal structure and organization of the enterprise, with business processes being the fundamental components of the enterprise operation. Such approaches generally assume enterprises as deterministic, top-down managed entities, with a well-defined group of processe作者: 低能兒 時間: 2025-3-22 12:29
Youjia Liu,Yasumasa Matsuda,Zhijie Zhangises, case studies and detailed references.Each method is co.This book draws new attention to domain-specific conceptual modeling by presenting the work of thought leaders who have designed and deployed specific modeling methods. It provides hands-on guidance on how to build models in a particular d作者: sparse 時間: 2025-3-22 13:50
Jiawei Hai,Zhen Xie,Na Liu,Ye Yuanint thinking’ that focuses on the formal structure and organization of the enterprise, with business processes being the fundamental components of the enterprise operation. Such approaches generally assume enterprises as deterministic, top-down managed entities, with a well-defined group of processe作者: 良心 時間: 2025-3-22 17:32 作者: 征稅 時間: 2025-3-22 23:11
Eman Saleh,Caroline Sabtyint thinking’ that focuses on the formal structure and organization of the enterprise, with business processes being the fundamental components of the enterprise operation. Such approaches generally assume enterprises as deterministic, top-down managed entities, with a well-defined group of processe作者: 脫離 時間: 2025-3-23 04:42 作者: Ordeal 時間: 2025-3-23 08:39 作者: Enliven 時間: 2025-3-23 09:59
Haiwen Chen,Songcan Yu,Shupeng Zhao,Junbo Wang,Kaiming Zhu,Kento Satoe portable, but an unidiomatic translation jeopardizes performance because, in practice, language implementations favor the common cases. This tension arises especially when the domain calls for complex control structures. We illustrate this tension by revisiting Landin’s original correspondence bet作者: 性冷淡 時間: 2025-3-23 16:52
Guangfei Qi,Zhihao Qu,Shen-Huan Lyu,Ninghui Jia,Baoliu Yeuage-independent account, covering object-oriented, functionThis textbook describes the theory and the pragmatics of using and engineering high-level software languages – also known as modeling or domain-specific languages (DSLs) – for creating quality software. This includes methods, design pattern作者: judiciousness 時間: 2025-3-23 21:49
Junhao Yin,Xiran Hu,Geng Zhao,Guochang Wanguage-independent account, covering object-oriented, functionThis textbook describes the theory and the pragmatics of using and engineering high-level software languages – also known as modeling or domain-specific languages (DSLs) – for creating quality software. This includes methods, design pattern作者: Decimate 時間: 2025-3-24 01:01
Chaohao Fu,Yun Zhou,Na Ruanrinciples have proven to be very effective in motivating target users in keeping their engagement within everyday challenges, including dedication to education, use of public transportation, adoption of healthy habits, and so forth. The spread of gameful applications and the consequent growth of the作者: Commemorate 時間: 2025-3-24 02:42 作者: BET 時間: 2025-3-24 08:04 作者: Thymus 時間: 2025-3-24 13:18 作者: 前兆 時間: 2025-3-24 17:19
PRICAI 2024: Trends in Artificial Intelligence978-981-96-0119-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Inoperable 時間: 2025-3-24 22:18
https://doi.org/10.1007/978-981-96-0119-6Artificial Intelligence; Machine Learning; Deep Learning; Agents; Decision Theory; Large Language Models; 作者: ear-canal 時間: 2025-3-24 23:12 作者: 分期付款 時間: 2025-3-25 07:09 作者: 閑逛 時間: 2025-3-25 08:10
Zero-Shot Heterogeneous Graph Embedding via?Semantic Extraction advantage of labeled data, showing promising performance. However, real-world datasets are frequently completely-imbalanced (i.e., zero-shot), wherein certain node types have no labeled instances. This scenario poses a formidable challenge for conventional graph embedding models, resulting in subop作者: 迅速飛過 時間: 2025-3-25 11:57 作者: 制定法律 時間: 2025-3-25 15:51 作者: 嫻熟 時間: 2025-3-25 20:25
SCBC: A Supervised Single-Cell Classification Method Based on Batch Correction for ATAC-Seq DataL) to cell classification tailored for scATAC-seq data. However, scATAC-seq data possess ambiguous feature spaces and sparse expression levels, and existing cell-typing methods typically either align modalities in the latent space or perform transfer learning based on scRNA-seq data. In this study, 作者: 散開 時間: 2025-3-26 02:00 作者: 滑稽 時間: 2025-3-26 08:12 作者: 拍下盜公款 時間: 2025-3-26 10:40
Federated Prompt Tuning: When is it Necessary?work of federated learning. This paper aims to answer “whether it is necessary to seek federation when clients already possess strong few-shot learning abilities with local prompt tuning” through experimental studies. We simulated various types of data distribution shifts that may exist among client作者: minimal 時間: 2025-3-26 16:23
Dirichlet-Based Local Inconsistency Query Strategy for?Active Domain Adaptationarget domain. In this process, uncertainty and representativeness are two crucial principles. Strategies focused on uncertainty seek to choose samples where the model’s predictions are less certain, whereas those centered on representativeness aim to pick samples that better reflect the overall data作者: exceptional 時間: 2025-3-26 20:25
FedSD: Cross-Heterogeneous Federated Learning Based on?Self-distillationer, in practical applications, IoT devices often train different sizes of models for different tasks. The heterogeneity among client model significantly affects the convergence and generalization performance of model. To enhance robustness in such heterogeneous scenarios, we introduce a novel FL fra作者: hedonic 時間: 2025-3-27 00:26
Personalized Federated Learning with?Feature Alignment via?Knowledge Distillationis a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature i作者: PHON 時間: 2025-3-27 05:07 作者: 橫條 時間: 2025-3-27 06:40
Preserving Individual User’s Right to?Be Forgotten in?Enterprise-Level Federated Learningir personal data from information service providers. While some prior studies have explored the problem of removing a client’s contribution in federated learning, there is a dearth of research considering the unlearning from the user’s perspective. In enterprise-level federated learning, a company p作者: limber 時間: 2025-3-27 10:02 作者: entreat 時間: 2025-3-27 14:17
Contrastive Prototype Network for?Generative Zero-Shot Learningtraints for unseen class visual features. To address this, we propose the Contrastive Prototype Network (CPNet). CPNet uses prototype learning to determine the feature vector center for each category (the prototype) and classifies based on the similarity between test data and prototypes. Concurrentl作者: 昆蟲 時間: 2025-3-27 19:29
Conference proceedings 2025 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.
