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检索条件"机构=Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE"
207 条 记 录,以下是61-70 订阅
排序:
A hybrid deep learning method for controlled stochastic Kolmogorov systems with regime-switching
A hybrid deep learning method for controlled stochastic Kolm...
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International Conference on Control, Decision and Information Technologies (CoDIT)
作者: Yu Zhang Zhuo Jin Jiaqin Wei School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai China Department of Actuarial Studies and Business Analytics Macquarie University NSW Australia
In this paper, we employ numerical methods based on deep learning algorithms for solving controlled stochastic Kolmogorov systems with regime-switching. Different from classical control problems, each component of the... 详细信息
来源: 评论
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels
arXiv
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arXiv 2022年
作者: Li, Siyuan Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China
Pre-trained vision-language models like CLIP have recently shown superior performances on various downstream tasks, including image classification and segmentation. However, in fine-grained image re-identification (Re... 详细信息
来源: 评论
Disentangling the spatial structure and style in conditional vae
arXiv
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arXiv 2019年
作者: Zhang, Ziye Sun, Li Zheng, Zhilin Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai200241 China
This paper aims to disentangle the latent space in cVAE into the spatial structure and the style code, which are complementary to each other, with one of them zs being label relevant and the other zu irrelevant. The g... 详细信息
来源: 评论
DYNAMIC-LLAVA: EFFICIENT MULTIMODAL LARGE LANGUAGE MODELS VIA DYNAMIC VISION-LANGUAGE CONTEXT SPARSIFICATION
arXiv
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arXiv 2024年
作者: Huang, Wenxuan Zhai, Zijie Shen, Yunhang Cao, Shaosheng Zhao, Fei Xu, Xiangfeng Ye, Zheyu Hu, Yao Lin, Shaohui East China Normal University China Xiamen University China Xiaohongshu Inc China Nanjing University China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE China
Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision understanding, reasoning, and interaction. However, the inference computation and memory increase progressively with the generation o... 详细信息
来源: 评论
QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation
arXiv
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arXiv 2022年
作者: Hu, Xueqi Zhou, Xinyue Huang, Qiusheng Shi, Zhengyi Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China
Unpaired image-to-image (I2I) translation often requires to maximize the mutual information between the source and the translated images across different domains, which is critical for the generator to keep the source... 详细信息
来源: 评论
Novel View Synthesis on Unpaired data by Conditional Deformable Variational Auto-Encoder
arXiv
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arXiv 2020年
作者: Yin, Mingyu Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing China Key Laboratory of Advanced Theory and Application in Statistics & Data Science East China Normal University Shanghai200241 China
Novel view synthesis often needs the paired data from both the source and target views. This paper proposes a view translation model under cVAE-GAN framework without requiring the paired data. We design a conditional ... 详细信息
来源: 评论
Dynamic Contrastive Knowledge Distillation for Efficient Image Restoration
arXiv
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arXiv 2024年
作者: Zhou, Yunshuai Qiao, Junbo Liao, Jincheng Li, Wei Li, Simiao Xie, Jiao Shen, Yunhang Hu, Jie Lin, Shaohui East China Normal University Shanghai China Huawei Noah’s Ark Lab China Xiamen University China Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE China
Knowledge distillation (KD) is a valuable yet challenging approach that enhances a compact student network by learning from a high-performance but cumbersome teacher model. However, previous KD methods for image resto... 详细信息
来源: 评论
Semi-supervised inference for block-wise missing data without imputation
The Journal of Machine Learning Research
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The Journal of Machine Learning Research 2024年 第1期25卷 4902-4937页
作者: Shanshan Song Yuanyuan Lin Yong Zhou School of Mathematical Sciences and School of Economics and Management Tongji University Shanghai China Department of Statistics The Chinese University of Hong Kong Hong Kong China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE Academy of Statistics and Interdisciplinary Sciences and School of Statistics East China Normal University Shanghai China
We consider statistical inference for single or low-dimensional parameters in a high-dimensional linear model under a semi-supervised setting, wherein the data are a combination of a labelled block-wise missing data s... 详细信息
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Distributed algorithms for u-statistics-based empirical risk minimization
The Journal of Machine Learning Research
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The Journal of Machine Learning Research 2023年 第1期24卷 12301-12343页
作者: Lanjue Chen Alan T.K. Wan Shuyi Zhang Yong Zhou Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education Academy of Statistics and Interdisciplinary Sciences and School of Statistics East China Normal University Shanghai China Department of Management Sciences School of Data Science and Department of Biostatistics City University of Hong Kong Kowloon Hong Kong
Empirical risk minimization, where the underlying loss function depends on a pair of data points, covers a wide range of application areas in statistics including pairwise ranking and survival analysis. The common emp...
来源: 评论
A data-driven line search rule for support recovery in high-dimensional data analysis
arXiv
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arXiv 2021年
作者: Li, Peili Jiao, Yuling Lu, Xiliang Kang, Lican School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai200062 China School of Mathematics and Statistics Hubei Key Laboratory of Computational Science Wuhan University Wuhan430072 China School of Mathematics and Statistics Wuhan University Wuhan430072 China
In this work, we consider the algorithm to the (nonlinear) regression problems with 0penalty. The existing algorithms for 0based optimization problem are often carried out with a fixed step size, and the selection of ... 详细信息
来源: 评论