机构

• 理论物理研究所 [5]
• 自动化研究所 [4]
• 合肥物质科学研究院 [4]
• 地理科学与资源研究所 [1]
• 计算技术研究所 [1]
• 化学研究所 [1]
• 更多

采集方式

• OAI收割 [22]

内容类型

• 期刊论文 [19]
• 会议论文 [3]

发表日期

• 2025 [1]
• 2024 [3]
• 2023 [2]
• 2022 [1]
• 2021 [3]
• 2019 [2]
• 更多

学科主题

• Physics [5]
• Plant Scie... [1]

筛选

浏览/检索结果: 共22条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 An LLM-Based Method for Quality Information Extraction From Web Text for Crowed-Sensing Spatiotemporal Data 期刊论文  OAI收割 TRANSACTIONS IN GIS, 2025, 卷号: 29, 期号: 1, 页码: e13294 作者:  Huang, Zongcai;  Peng, Peng;  Lu, Feng;  Zhang, He   |  收藏  |  浏览/下载：93/0  |  提交时间：2025/02/24 crowd-sensing  large language model  prompt fine-tuning technique  quality indicators  spatiotemporal data   Embedded prompt tuning: Towards enhanced calibration of pretrained models for medical images 期刊论文  OAI收割 MEDICAL IMAGE ANALYSIS, 2024, 卷号: 97, 页码: 13 作者:  Zu, Wenqiang;  Xie, Shenghao;  Zhao, Qing;  Li, Guoqi;  Ma, Lei   |  收藏  |  浏览/下载：8/0  |  提交时间：2024/09/09 Foundation model  Parameter-efficient fine-tuning  Visual prompt tuning  Few-shot medical image analysis   Embracing Large Natural Data: Enhancing Medical Image Analysis via Cross-Domain Fine-Tuning 期刊论文  OAI收割 IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2024, 卷号: 28 作者:  Li, Qiankun;  Huang, Xiaolong;  Fang, Bo;  Chen, Huabao;  Ding, Siyuan   |  收藏  |  浏览/下载：5/0  |  提交时间：2024/11/20 Large natural data  medical image  cross-domain learning  staged fine-tuning  Large natural data  medical image  cross-domain learning  staged fine-tuning   Training Large Language Models to Follow System Prompt with Self-Supervised Fine-Tuning 会议论文  OAI收割 YOKOHAMA, JAPAN, 2024-07 作者:  Junyan Qiu;  Haitao Wang;  Yiping Yang   |  收藏  |  浏览/下载：14/0  |  提交时间：2024/06/17 large language models  supervised fine-tuning  instruct tuning  stylized generation   Zero-shot language extension for dialogue state tracking via pre-trained models and multi-auxiliary-tasks fine-tuning 期刊论文  OAI收割 KNOWLEDGE-BASED SYSTEMS, 2023, 卷号: 259, 页码: 14 作者:  Xiang, Lu;  Zhao, Yang;  Zhu, Junnan;  Zhou, Yu;  Zong, Chengqing   |  收藏  |  浏览/下载：20/0  |  提交时间：2023/03/20 Dialogue state tracking  Zero -shot language extension  Multilingual DST  Pre -trained models  Multi -auxiliary -tasks fine-tuning   结合感受野模块与并联RPN网络的火焰检测 期刊论文  OAI收割 中国图象图形学报, 2023, 卷号: 28 作者:  鲍文霞;  孙强;  梁栋;  胡根生;  杨先军   |  收藏  |  浏览/下载：11/0  |  提交时间：2023/11/10 flame detection  deep learning  receptive field(RF)  parallel region proposal network(PRPN)  negative sample fine-tuning  火焰检测  深度学习  感受野(RF)  并联区域建议网络(PRPN)  负样本微调   Modeling and Analysis of Vertical Angle Fine-tuning Mechanism Based on Bridge-type Mechanism 会议论文  OAI收割 Nanchang, Virtual, China, 2022-12-18 作者:  Jiang, Bo;  Zhang, Zijie;  Zhou, Shun;  Chu, Yuanbo;  Guo, Yifan   |  收藏  |  浏览/下载：23/0  |  提交时间：2023/03/13 Angle Fine tuning  bridge type Mechanism  compliance matrix method  finite element method   Design of quasi-axisymmetric stellarators with variable-thickness perpendicular permanent magnets based on a two-step magnet design strategy 期刊论文  OAI收割 NUCLEAR FUSION, 2021, 卷号: 61 作者:  Lu, Z. Y.;  Xu, G. S.;  Chen, D. H.;  Chen, L.;  Zhang, X. Y.   |  收藏  |  浏览/下载：33/0  |  提交时间：2021/11/01 stellarator  permanent magnet  two-step magnet design strategy  local compensation method  global fine-tuning method   Design?of quasi-axisymmetric stellarators with variable-thickness perpendicular permanent magnets based on a two-step magnet design?strategy 期刊论文  OAI收割 Nuclear Fusion, 2021, 卷号: 61 作者:  Lu,Z.Y.;  Xu,G.S.;  Chen,D.H.;  Chen,L.;  Zhang,X.Y.   |  收藏  |  浏览/下载：24/0  |  提交时间：2021/11/01 stellarator  permanent magnet  two-step magnet design ?strategy  local compensation method  global fine-tuning method   pDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning 期刊论文  OAI收割 JOURNAL OF PROTEOME RESEARCH, 2021, 卷号: 20, 期号: 5, 页码: 2570-2582 作者:  Chen, Zhen-Lin;  Mao, Peng-Zhi;  Zeng, Wen-Feng;  Chi, Hao;  He, Si-Min   |  收藏  |  浏览/下载：35/0  |  提交时间：2021/12/01 cross-linking mass spectrometry  spectrum prediction  deep learning  transfer learning  online fine-tuning