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检索条件"机构=MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"
231 条 记 录,以下是91-100 订阅
排序:
Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks
arXiv
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arXiv 2023年
作者: Li, Ye Chen, Song-Can Huang, Sheng-Jun College of Computer Science and Technology/Artificial Intelligence Nanjing University of Aeronautics Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China
Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems, but they are still trapped in training failures when the target functions to b... 详细信息
来源: 评论
All Beings Are Equal in Open Set Recognition
arXiv
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arXiv 2024年
作者: Li, Chaohua Zhang, Enhao Geng, Chuanxing Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China
In open-set recognition (OSR), a promising strategy is exploiting pseudo-unknown data outside given K known classes as an additional K+1-th class to explicitly model potential open space. However, treating unknown cla... 详细信息
来源: 评论
TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series analysis
arXiv
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arXiv 2024年
作者: Liu, Jiexi Cao, Meng Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China
Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Due to their non-uniform intervals between successive observations and varying sampling rates among series, the channel-independent (CI) s... 详细信息
来源: 评论
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels
arXiv
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arXiv 2024年
作者: Zong, Chen-Chen Wang, Ye-Wen Xie, Ming-Kun Huang, Sheng-Jun College of Computer Science and Technology/Artificial Intelligence Nanjing University of Aeronautics Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China
Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs). Existing approaches address this issue through loss correction or example selection methods. However, ... 详细信息
来源: 评论
DouGNN: An End-to-End Deep Learning Framework for Predicting Individual Behaviors from fMRI Data
DouGNN: An End-to-End Deep Learning Framework for Predicting...
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Image Processing, Computer Vision and machine Learning (ICICML), International Conference on
作者: Qumei Cao Xuyun Wen College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China
Predicting individual behavior from functional connectivity (FC) using machine learning is a critical research topic in neuroscience. While various models have been proposed, they mainly focus on designing behavior pr...
来源: 评论
AADL and Modelica model combination and model conversion based on CPS  4
AADL and Modelica model combination and model conversion bas...
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4th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2020
作者: Zhu, Yifeng Cao, Zining Wang, Fujun Lu, Weiwei College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China College of Computer Science and Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Miit Key Laboratory of Pattern Analysis and Machine Intelligence Luoyang China Science and Technology on Electro-optic Control Laboratory Luoyang China
Cyber-Physical System (CPS), which realizes the close integration of physical resources and information resources, is a distributed and asynchronous dynamic hybrid system running in different time and space. In this p... 详细信息
来源: 评论
View-labels Are Indispensable: A Multifacet Complementarity Study of Multi-view Clustering
arXiv
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arXiv 2022年
作者: Geng, Chuanxing Han, Aiyang Chen, Songcan The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China
Consistency and complementarity are two key ingredients for boosting multi-view clustering (MVC). Recently with the introduction of popular contrastive learning, the consistency learning of views has been further enha...
来源: 评论
Personalized Federated Semi-Supervised Learning with Black-Box Models
Personalized Federated Semi-Supervised Learning with Black-B...
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IEEE International Conference on Data Mining (ICDM)
作者: Siyin Huang Shao-Yuan Li Songcan Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China
Federated Semi-Supervised Learning alleviates the necessity for fully labeled data in Federated Learning. However, it does not sufficiently prioritize model privacy or the personalized requirements of clients. To addr...
来源: 评论
MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series analysis
arXiv
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arXiv 2024年
作者: Liu, Jiexi Cao, Meng Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautic China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China
Irregularly sampled multivariate time series (ISMTS) are prevalent in reality. Most existing methods treat ISMTS as synchronized regularly sampled time series with missing values, neglecting that the irregularities ar... 详细信息
来源: 评论
Contextual Conservative Q-Learning for Offline Reinforcement Learning
arXiv
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arXiv 2023年
作者: Jiang, Ke Yao, Jiayu Tan, Xiaoyang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China
Offline reinforcement learning learns an effective policy on offline datasets without online interaction, and it attracts persistent research attention due to its potential of practical application. However, extrapola... 详细信息
来源: 评论