咨询与建议

限定检索结果

文献类型

  • 211 篇 期刊文献
  • 87 篇 会议
  • 3 册 图书

馆藏范围

  • 301 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 196 篇 工学
    • 119 篇 计算机科学与技术...
    • 96 篇 软件工程
    • 42 篇 生物医学工程(可授...
    • 42 篇 生物工程
    • 32 篇 光学工程
    • 28 篇 信息与通信工程
    • 24 篇 电气工程
    • 20 篇 控制科学与工程
    • 17 篇 电子科学与技术(可...
    • 8 篇 化学工程与技术
    • 7 篇 机械工程
    • 7 篇 仪器科学与技术
    • 7 篇 建筑学
    • 7 篇 土木工程
    • 7 篇 安全科学与工程
  • 136 篇 理学
    • 59 篇 数学
    • 48 篇 生物学
    • 44 篇 物理学
    • 34 篇 统计学(可授理学、...
    • 17 篇 地球物理学
    • 12 篇 系统科学
  • 51 篇 管理学
    • 29 篇 管理科学与工程(可...
    • 19 篇 工商管理
    • 19 篇 图书情报与档案管...
  • 43 篇 医学
    • 38 篇 临床医学
    • 30 篇 基础医学(可授医学...
    • 18 篇 药学(可授医学、理...
    • 13 篇 公共卫生与预防医...
  • 9 篇 经济学
    • 9 篇 应用经济学
  • 7 篇 农学
  • 6 篇 法学
    • 6 篇 社会学
  • 3 篇 教育学

主题

  • 12 篇 machine learning
  • 11 篇 deep learning
  • 9 篇 image segmentati...
  • 8 篇 predictive model...
  • 7 篇 feature extracti...
  • 7 篇 accuracy
  • 6 篇 training
  • 5 篇 galaxies
  • 5 篇 neural networks
  • 5 篇 cosmology
  • 5 篇 artificial intel...
  • 5 篇 tumors
  • 4 篇 object detection
  • 4 篇 task analysis
  • 4 篇 graph neural net...
  • 4 篇 computational mo...
  • 4 篇 benchmarking
  • 4 篇 diseases
  • 3 篇 learning systems
  • 3 篇 reinforcement le...

机构

  • 35 篇 center for data ...
  • 34 篇 center for machi...
  • 12 篇 beijing internat...
  • 11 篇 center for compu...
  • 9 篇 national enginee...
  • 9 篇 national biomedi...
  • 9 篇 ciela - montreal...
  • 8 篇 peking universit...
  • 8 篇 university of pe...
  • 7 篇 department of ra...
  • 7 篇 machine learning...
  • 7 篇 department of ph...
  • 7 篇 peking universit...
  • 7 篇 heidelberg
  • 6 篇 lab crestview ra...
  • 6 篇 helmholtz imagin...
  • 6 篇 translatum - cen...
  • 6 篇 school of mathem...
  • 6 篇 department of pa...
  • 6 篇 faculty of mathe...

作者

  • 11 篇 hezaveh yashar
  • 11 篇 wang liwei
  • 10 篇 li zhang
  • 10 篇 perreault-levass...
  • 10 篇 bin dong
  • 9 篇 bakas spyridon
  • 8 篇 li hongwei bran
  • 8 篇 kofler florian
  • 8 篇 ulrich constanti...
  • 8 篇 zifan chen
  • 7 篇 jie zhao
  • 7 篇 ezhov ivan
  • 7 篇 linguraru marius...
  • 7 篇 menze bjoern
  • 6 篇 hüllermeier eyke
  • 6 篇 wang chunhao
  • 6 篇 kazerooni anahit...
  • 6 篇 chung verena
  • 6 篇 dong bin
  • 6 篇 moawad ahmed w.

语言

  • 274 篇 英文
  • 25 篇 其他
  • 1 篇 中文
检索条件"机构=Research Center of Machine Learning and Data Analysis"
301 条 记 录,以下是71-80 订阅
排序:
Pitfalls of epistemic uncertainty quantification through loss minimisation  22
Pitfalls of epistemic uncertainty quantification through los...
收藏 引用
Proceedings of the 36th International Conference on Neural Information Processing Systems
作者: Viktor Bengs Eyke Hüllermeier Willem Waegeman Institute of Informatics University of Munich (LMU) Institute of Informatics University of Munich (LMU) and Munich Center for Machine Learning Department of Data Analysis and Mathematical Modeling Ghent University
Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The l...
来源: 评论
EPR-Net: constructing a non-equilibrium potential landscape via a variational force projection formulation
收藏 引用
National Science Review 2024年 第7期11卷 135-147页
作者: Yue Zhao Wei Zhang Tiejun Li Center for Data Science Peking University Zuse Institute Berlin Department of Mathematics and Computer Science Freie Universit?t Berlin Laboratory of Mathematics and Applied Mathematics(LMAM)and School of Mathematical Sciences Peking University Center for Machine Learning Research Peking University
We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state ***-Net leverages a ... 详细信息
来源: 评论
Method for Finding an Investment Strategy in the Case of a Sparse Covariance Matrix
Method for Finding an Investment Strategy in the Case of a S...
收藏 引用
International Conference on Management of Large-Scale System Development (MLSD)
作者: Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University under the Government of the Russian Federation Moscow Russia
An optimality principle is proposed for making investment decisions based on efficiency and risk assessments with a sparse covariance matrix. The method is implemented as a program with a graphical interface and demon... 详细信息
来源: 评论
Investor Risk Profile Determination Model
Investor Risk Profile Determination Model
收藏 引用
International Conference on Management of Large-Scale System Development (MLSD)
作者: Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia
An assessment of the investor’s risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimiz...
来源: 评论
How Transformers Get Rich: Approximation and Dynamics analysis
arXiv
收藏 引用
arXiv 2024年
作者: Wang, Mingze Yu, Ruoxi Weinan, E. Wu, Lei School of Mathematical Sciences Peking University China School of Mathematical Sciences Center for Machine Learning Research AI for Science Institute Peking University China Center for Data Science Peking University China School of Mathematical Sciences Center for Machine Learning Research Peking University China
Transformers have demonstrated exceptional in-context learning capabilities, yet the theoretical understanding of the underlying mechanisms remains limited. A recent work (Elhage et al., 2021) identified a "rich&... 详细信息
来源: 评论
Multi-Scale Clinical-Guided Binocular Fusion Framework for Predicting New-Onset Hypertension Over a Four-Year Period
Multi-Scale Clinical-Guided Binocular Fusion Framework for P...
收藏 引用
IEEE International Symposium on Biomedical Imaging
作者: Haoshen Li Zifan Chen Jie Zhao Heyun Chen Hexin Dong Mingze Yuan Bin Dong Li Zhang Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China
Hypertension is a major global health concern, linked to various cardiovascular diseases and associated with distinct ocular manifestations. While recent advances in artificial intelligence have enabled accurate diagn... 详细信息
来源: 评论
Object Segmentation Tracking from Generic Video Cues
Object Segmentation Tracking from Generic Video Cues
收藏 引用
International Conference on Pattern Recognition
作者: Amirhossein Kardoost Sabine Müller Joachim Weickert Margret Keuper Data and Web Science Group University of Mannheim Mannheim Germany Fraunhofer Center Machine Learning Germany Mathematical Image Analysis Group Saarland University Saarbrücken Germany
We propose a light-weight variational framework for online tracking of object segmentations in videos based on optical flow and image boundaries. While high-end computer vision methods on this task rely on sequence sp... 详细信息
来源: 评论
Revealing excited states of rotational Bose-Einstein condensates
收藏 引用
The Innovation 2024年 第1期5卷 41-48页
作者: Jianyuan Yin Zhen Huang Yongyong Cai Qiang Du Lei Zhang School of Mathematical Sciences Laboratory of Mathematics and Applied MathematicsPeking UniversityBeijing 100871China Department of Mathematics National University of SingaporeSingapore 119076Singapore Department of Mathematics University of CaliforniaBerkeleyBerkeleyCA 94720USA School of Mathematical Sciences Beijing Normal UniversityBeijing 100875China Department of Applied Physics and Applied Mathematics and Data Science Institute Columbia UniversityNew YorkNY 10027USA Beijing International Center for Mathematical Research Center for Quantitative BiologyCenter for Machine Learning ResearchPeking UniversityBeijing 100871China
Rotational Bose-Einstein condensates can exhibit quantized vortices as topological *** this study,the ground and excited states of the rotational Bose-Einstein condensates are systematically studied by calculating the... 详细信息
来源: 评论
ENHANCING ZEROTH-ORDER FINE-TUNING FOR LANGUAGE MODELS WITH LOW-RANK STRUCTURES
arXiv
收藏 引用
arXiv 2024年
作者: Chen, Yiming Zhang, Yuan Cao, Liyuan Yuan, Kun Wen, Zaiwen Beijing International Center for Mathematical Research Peking University Beijing China Center for Data Science Peking University Beijing China Center for Machine Learning Research Peking University Beijing China
Parameter-efficient fine-tuning (PEFT) significantly reduces memory costs when adapting large language models (LLMs) for downstream applications. However, traditional first-order (FO) fine-tuning algorithms incur subs...
来源: 评论
LimeSoDa: A dataset Collection for Benchmarking of machine learning Regressors in Digital Soil Mapping
arXiv
收藏 引用
arXiv 2025年
作者: Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara
Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are neede... 详细信息
来源: 评论