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Multi-level sparse network lasso: Locally sparse learning with flexible sample clusters
期刊论文
NEUROCOMPUTING, 2025, 卷号: 635
作者:
Fei, Luhuan
;
Wang, Xinyi
;
Wang, Jiankun
;
Sun, Lu
;
Zhang, Yuyao
Adobe PDF(3646Kb)
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收藏
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浏览/下载:64/1
|
提交时间:2025/03/28
Adversarial machine learning
Contrastive Learning
Clusterings
Features selection
Group level
Multi-level network lasso
Multi-level networks
Multilevels
Sample clustering
Sparse learning
Sparse network
Traditional learning
Metabolomic profiles underlying gout flares: a prospective study of people with gout
期刊论文
RMD OPEN, 2025, 卷号: 11, 期号: 2
作者:
Sun, Wenyan
;
Li, Rui
;
Dalbeth, Nicola
;
Cui, Lingling
;
Liu, Zhen
Adobe PDF(8939Kb)
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收藏
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浏览/下载:5/1
|
提交时间:2025/05/20
Gout
Inflammation
Machine Learning
Robust multi-view subspace clustering with missing data by aligning nonlinear manifolds
期刊论文
PATTERN RECOGNITION, 2025, 卷号: 161
作者:
Mao, Zhan-Wang
;
Sun, Lu
;
Wu, Youlong
Adobe PDF(1513Kb)
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收藏
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浏览/下载:154/3
|
提交时间:2025/01/10
Adversarial machine learning
Contrastive Learning
Data assimilation
Spatio-temporal data
High-dimensional
Higher-dimensional
Incomplete view
Missing data
Multi-view learning
Multi-views
Multiple views
Nonlinear manifolds
Subspace clustering
Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data
期刊论文
MEDICAL IMAGE ANALYSIS, 2025, 卷号: 102
作者:
Pistos, Michalis
;
Li, Gang
;
Lin, Weili
;
Shen, Dinggang
;
Rekik, Islem
Adobe PDF(5127Kb)
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收藏
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浏览/下载:43/1
|
提交时间:2025/04/11
Adversarial machine learning
Contrastive Learning
Graph neural networks
Prediction models
Brain connectivity
Connectomics
Data scarcity
Graph neural networks
Learning frameworks
Longitudinal connectomic dataset
Multi-modal
Multimodal brain graph evolution prediction
Single-baseline
Trajectory prediction
Synergistic effects of confinement size and interface on anomalous ultrafast transport in nanofluidics
期刊论文
COMMUNICATIONS PHYSICS, 2025, 卷号: 8, 期号: 1
作者:
Adobe PDF(2247Kb)
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收藏
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浏览/下载:22/3
|
提交时间:2025/05/06
Molecular dynamics
Nanoclay
Dynamics simulation
Graphenes
Machine
learning
Molecular origins
Nanoslits
Parallel transport
Surface interactions
Synergistic effect
Transport behavior
Ultra
fast
RankTuner: When Design Tool Parameter Tuning Meets Preference Bayesian Optimization
会议论文
IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN, DIGEST OF TECHNICAL PAPERS, ICCAD, Newark Liberty International Airport, 1 Hotel Rd, New York, NY, United states, October 27, 2024 - October 31, 2024
作者:
Xu, Peng
;
Zheng, Su
;
Ye, Yuyang
;
Bai, Chen
;
Xu, Siyuan
收藏
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浏览/下载:7/0
|
提交时间:2025/05/30
Computer aided logic design
Bayesian optimization
Design tool
Electronic design automation tools
Feature interactions
Machine learning methods
Parameters tuning
Search spaces
Statistical learning methods
Tool parameter
Verylarge-scale integrations (VLSI)
无权访问的条目
会议论文
作者:
Liu, Qianyu(刘芊渝)
;
Li, Xinran(李欣然)
;
Du, Xiaocong(杜晓聪)
;
Li, Quan(李权)
Adobe PDF(6100Kb)
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收藏
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浏览/下载:24/5
|
提交时间:2025/03/12
Conveniently Identify Coils in Inductive Power Transfer System Using Machine Learning
会议论文
2025 IEEE APPLIED POWER ELECTRONICS CONFERENCE AND EXPOSITION (APEC), Atlanta, GA, USA, 16-20 March 2025
作者:
Yifan Zhao
;
Mowei Lu
;
Ting Chen
;
Heyuan Li
;
Xiang Gao
Adobe PDF(19239Kb)
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收藏
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浏览/下载:12/1
|
提交时间:2025/05/06
Channel estimation
Computer software selection and evaluation
Direction of arrival
Time difference of arrival
Time of arrival
Verification
High frequency HF
Higher efficiency
Inductive power transfer systems
Inductive powertransfer (IPT)
Machine-learning
Parameters identification
Simulation software
Transmission distances
Very high frequency
Very-high-frequency inductive power transfer
DropNaE: Alleviating irregularity for large-scale graph representation learning
期刊论文
NEURAL NETWORKS, 2025, 卷号: 183
作者:
Liu, Xin
;
Xiong, Xunbin
;
Yan, Mingyu
;
Xue, Runzhen
;
Pan, Shirui
Adobe PDF(1802Kb)
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收藏
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浏览/下载:123/2
|
提交时间:2024/12/27
Adversarial machine learning
Federated learning
Graph algorithms
Knowledge graph
Algorithm on graph representation learning
Efficient large-scale graph representation learning
Graph neural networks
Graph representation
Irregularity
Large-scales
Neural networks trainings
Real-world graphs
Real-world scenario
Enhancing reliability in photonuclear cross-section fitting with Bayesian neural networks
期刊论文
NUCLEAR SCIENCE AND TECHNIQUES, 2025, 卷号: 36, 期号: 3
作者:
Sun, Qian-Kun
;
Zhang, Yue
;
Hao, Zi-Rui
;
Wang, Hong-Wei
;
Fan, Gong-Tao
Adobe PDF(2127Kb)
|
收藏
|
浏览/下载:67/1
|
提交时间:2025/03/03
Photoneutron reaction
Bayesian neural network
Machine learning
Gamma source
SLEGS
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