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MoME: Mixture-of-Masked-Experts for Efficient Multi-Task Recommendation
会议论文
SIGIR 2024 - PROCEEDINGS OF THE 47TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, Washington, DC, United states, July 14, 2024 - July 18, 2024
作者:
Xu, Jiahui
;
Sun, Lu
;
Zhao, Dengji
Adobe PDF(1222Kb)
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浏览/下载:85/0
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提交时间:2024/08/23
Coarse-grained modeling
Computational efficiency
Learning systems
Binary masks
Learning techniques
Mixture of experts
Model mixtures
Multi tasks
Multi-task recommendation
Multitask learning
Parameter sharing
Real-world datasets
Subnetworks
Layer-dependent magnon-magnon coupling in a synthetic antiferromagnet
期刊论文
PHYSICAL REVIEW APPLIED, 2024, 卷号: 21, 期号: 5
作者:
Rong, Yaqi
;
Jiang, Chengxin
;
Wang, Huishan
;
Sun, Lu
;
Liu, Fengyu
Adobe PDF(1992Kb)
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浏览/下载:122/1
  |  
提交时间:2024/06/07
Antiferromagnetic materials
Anticrossings
Coupling strengths
Even layers
Hybridisation
Layer number
Optical modes
Quantum computing systems
Quasiparticles
Resonance spectrum
Synthetic antiferromagnets
Redirected transfer learning for robust multi-layer subspace learning
期刊论文
PATTERN ANALYSIS AND APPLICATIONS, 2024, 卷号: 27, 期号: 1
作者:
Bao, Jiaqi
;
Kudo, Mineichi
;
Kimura, Keigo
;
Sun, Lu
Adobe PDF(3535Kb)
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浏览/下载:103/0
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提交时间:2024/05/14
Transfer learning
Robustness
Sparsity
Regression
Discriminative
Robust embedding regression for semi-supervised learning
期刊论文
PATTERN RECOGNITION, 2024, 卷号: 145
作者:
Bao, Jiaqi
;
Kudo, Mineichi
;
Kimura, Keigo
;
Sun, Lu
Adobe PDF(2659Kb)
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浏览/下载:257/0
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提交时间:2023/10/13
Classification (of information)
Feature Selection
Iterative methods
Regression analysis
Embeddings
Features selection
Labeled data
Low-rank representations
Nuclear norm
Real-world
Ridge regression
Semi-supervised learning
Semi-supervised method
Unlabeled data
Learning Low-Rank Tensor Cores with Probabilistic 0-Regularized Rank Selection for Model Compression
会议论文
IJCAI INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, Jeju, Korea, Republic of, August 3, 2024 - August 9, 2024
作者:
Cao, Tianxiao
;
Sun, Lu
;
Nguyen, Canh Hao
;
Mamitsuka, Hiroshi
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浏览/下载:42/0
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提交时间:2024/10/11
Incomplete Multi-view Weak-Label Learning with Noisy Features and Imbalanced Labels
会议论文
LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS), Jakarta, Indonesia, November 15, 2023 - November 19, 2023
作者:
Li, Zhiwei
;
Yang, Zijian
;
Sun, Lu
;
Kudo, Mineichi
;
Kimura, Keigo
Adobe PDF(737Kb)
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浏览/下载:164/28
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提交时间:2024/01/19
Embeddings
Focal loss
Hilbert-schmidt independence criterions
Modern applications
Multi-label learning
Multi-view multi-label learning
Multi-views
Multiple labels
Weak labels
Weakly supervised learning
Partial Multi-label Learning with a Few Accurately Labeled Data
会议论文
LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS), Jakarta, Indonesia, November 15, 2023 - November 19, 2023
作者:
Mizuguchi, Haruhi
;
Kimura, Keigo
;
Kudo, Mineichi
;
Sun, Lu
Adobe PDF(31537Kb)
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浏览/下载:130/0
  |  
提交时间:2024/01/19
Learning algorithms
Learning systems
Machine learning
False positive
Labeled data
Learn+
Machine-learning
Multi-label classifications
Multi-label learning
Partial multi-label learning
Ranking loss
Training data
Multi-Label Personalized Classification via Exclusive Sparse Tensor Factorization
会议论文
2023 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), Shanghai, China, 1-4 Dec. 2023
作者:
Weijia Lin
;
Jiankun Wang
;
Lu Sun
;
Mineichi Kudo
;
Keigo Kimura
Adobe PDF(784Kb)
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浏览/下载:140/0
  |  
提交时间:2023/11/22
Multi-Label Classification
Personalized Learning
Exclusive Sparse Tensor Factorization
Structured Sparse Multi-Task Learning with Generalized Group Lasso
会议论文
FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS, Krakow, Poland, September 30, 2023 - October 4, 2023
作者:
Fei, Luhuan
;
Sun, Lu
;
Kudo, Mineichi
;
Kimura, Keigo
Adobe PDF(934Kb)
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浏览/下载:392/145
  |  
提交时间:2023/11/24
Learning systems
Machine learning
Mathematical operators
Optimization
Compact model
Generalisation
Group lassos
High-dimensional
Learning to learn
Multitask learning
Optimization algorithms
Regularisation
Sharing information
Structured sparsities
Multiplicative Sparse Tensor Factorization for Multi-View Multi-Task Learning
会议论文
FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS, Krakow, Poland, September 30, 2023 - October 4, 2023
作者:
Wang, Xinyi
;
Sun, Lu
;
Nguyen, Canh Hao
;
Mamitsuka, Hiroshi
Adobe PDF(783Kb)
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浏览/下载:330/118
  |  
提交时间:2023/11/24
Factorization
Learning systems
Machine learning
Heterogeneous data
Learn+
Learning methods
Multi-views
Multiple tasks
Multiple views
Multitask learning
Predictive information
Sparse tensors
Tensor factorization