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检索条件"机构=MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"
228 条 记 录,以下是31-40 订阅
排序:
Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends  37
Beyond Myopia: Learning from Positive and Unlabeled Data thr...
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37th Conference on Neural Information Processing Systems, NeurIPS 2023
作者: Wang, Xinrui Wan, Wenhai Geng, Chuanxing Li, Shaoyuan Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China
Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of re... 详细信息
来源: 评论
An Improved AdaBoost Method in Imbalanced Data Learning
An Improved AdaBoost Method in Imbalanced Data Learning
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2023 International Conference on Cyber-Physical Social intelligence, ICCSI 2023
作者: Li, Ting Chen, Xiaofeng Li, Weikai Chongqing Jiaotong University Department of Mathematics Chongqing China Miit Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China
Learning from imbalanced datasets has recently received increasing research attention. Despite the remarkable results of AdaBoost in balanced situation, the imbalance problem remains to be solved. To address this, thi... 详细信息
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Robust AUC maximization for classification with pairwise confidence comparisons
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Frontiers of Computer Science 2024年 第4期18卷 73-83页
作者: Haochen SHI Mingkun XIE Shengjun HUANG College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing 211106China
Supervised learning often requires a large number of labeled examples,which has become a critical bottleneck in the case that manual annotating the class labels is *** mitigate this issue,a new framework called pairwi... 详细信息
来源: 评论
Medical Report Generation Based on Segment-Enhanced Contrastive Representation Learning  12th
Medical Report Generation Based on Segment-Enhanced Contras...
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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023
作者: Zhao, Ruoqing Wang, Xi Dai, Hongliang Gao, Pan Li, Piji College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China
Automated radiology report generation has the potential to improve radiology reporting and alleviate the workload of radiologists. However, the medical report generation task poses unique challenges due to the limited... 详细信息
来源: 评论
Sign-aware Perturbations Regression
Sign-aware Perturbations Regression
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2021 SIAM International Conference on Data Mining, SDM 2021
作者: Li, Zhongnian Zhang, Tao Shao, Wei Chen, Songcan Zhang, Daoqiang MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China
This paper presents the first study on Sign-aware Perturbations Regression (SaPR), where the observed response variables contain the aware sign (negative or positive) perturbations. In order to predict the non-perturb...
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Graph Hyperalignment for Multi-subject fMRI Functional Alignment  1
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1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
作者: Li, Weida Chen, Fang Zhang, Daoqiang College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China
In fMRI analysis, the scientist seeks to aggregate multi-subject fMRI data so that inferences shared across subjects can be achieved. The challenge is to eliminate the variability of anatomical structure and functiona... 详细信息
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Adaptive Joint Attention with Reinforcement Training for Convolutional Image Caption  1st
Adaptive Joint Attention with Reinforcement Training for C...
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1st International Workshop on Human Brain and Artificial intelligence, HBAI 2019, held in conjunction with the 28th International Joint Conference on Artificial intelligence, IJCAI 2019
作者: Chen, Ruoyu Li, Zhongnian Zhang, Daoqiang College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China
A convolutional decoder for image caption has proven to be easier to train than the Long Short Term Memory (LSTM) decoder [2]. However, previous convolutional image captioning methods are not good at capture the relat... 详细信息
来源: 评论
Detecting differential transcript usage across multiple conditions for RNA-seq data based on the smoothed LDA model
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Frontiers of Computer Science 2021年 第3期15卷 217-219页
作者: Jing LI Xuejun LIU Daoqiang ZHANG College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsMIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China
1 Introduction and main contributions Differential transcript usage(DTU),which refers to the event that the relative transcript abundance within a gene changes between *** detect DTU,various methods have been proposed... 详细信息
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Boundary Data Augmentation for Offline Reinforcement Learning
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ZTE Communications 2023年 第3期21卷 29-36页
作者: SHEN Jiahao JIANG Ke TAN Xiaoyang College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing 211106China
Offline reinforcement learning(ORL)aims to learn a rational agent purely from behavior data without any online *** of the major challenges encountered in ORL is the problem of distribution shift,i.e.,the mismatch betw... 详细信息
来源: 评论
A Multi-Scale Multi-Hop Graph Convolution Network for Predicting Fluid intelligence via Functional Connectivity
A Multi-Scale Multi-Hop Graph Convolution Network for Predic...
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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
作者: Wen, Xuyun Cao, Qumei Zhang, Daoqiang Nanjing University of Aeronautics and Astronautics Nanjing College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Jiangsu China
Predicting fluid intelligence via neuroimaging data is important to understand neural mechanisms underlying diverse complex cognitive tasks in human brain. Functional connectivity (FC) reflects interactions among brai... 详细信息
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