检索结果-内蒙古大学图书馆

A review of distributed statistical inference

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Gao, Yuan Liu, Weidong Wang, Hansheng Wang, Xiaozhou Yan, Yibo Zhang, Riquan School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE East China Normal University Shanghai China School of Mathematical Sciences Key Lab of Artificial Intelligence - MOE Shanghai Jiao Tong University Shanghai China Guanghua School of Management Peking University Beijing China

The rapid emergence of massive datasets in various fields poses a serious challenge to traditional statistical methods. Meanwhile, it provides opportunities for researchers to develop novel algorithms. Inspired by the idea of divide-and-conquer, various distributed frameworks for statistical estimation and inference have been proposed. They were developed to deal with large-scale statistical optimization problems. This paper aims to provide a comprehensive review for related literature. It includes parametric models, nonparametric models, and other frequently used models. Their key ideas and theoretical properties are summarized. The trade-off between communication cost and estimate precision together with other concerns are discussed. Copyright © 2023, The Authors. All rights reserved.

关键词： Principal component analysis

RecursiveDet: End-to-End Region-based Recursive Object Detection

学校读者我要写书评

暂无评论

RecursiveDet: End-to-End Region-based Recursive Object Detec...

International Conference on Computer Vision (ICCV)

作者： Jing Zhao Li Sun Qingli Li Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China

End-to-end region-based object detectors like Sparse R-CNN usually have multiple cascade bounding box decoding stages, which refine the current predictions according to their previous results. Model parameters within each stage are independent, evolving a huge cost. In this paper, we find the general setting of decoding stages is actually redundant. By simply sharing parameters and making a recursive decoder, the detector already obtains a significant improvement. The recursive decoder can be further enhanced by positional encoding (PE) of the proposal box, which makes it aware of the exact locations and sizes of input bounding boxes, thus becoming adaptive to proposals from different stages during the recursion. Moreover, we also design centerness-based PE to distinguish the RoI feature element and dynamic convolution kernels at different positions within the bounding box. To validate the effectiveness of the proposed method, we conduct intensive ablations and build the full model on three recent mainstream region-based detectors. The RecusiveDet is able to achieve obvious performance boosts with even fewer model parameters and slightly increased computation cost. Codes are available at https://***/bravezzzzzz/RecursiveDet.

关键词：

RecursiveDet: End-to-End Region-based Recursive Object Detection

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhao, Jing Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China

关键词： Object detection

IoU-Enhanced Attention for End-to-End Task Specific Object Detection

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhao, Jing Wu, Shengjian Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China

Without densely tiled anchor boxes or grid points in the image, sparse R-CNN achieves promising results through a set of object queries and proposal boxes updated in the cascaded training manner. However, due to the sparse nature and the one-to-one relation between the query and its attending region, it heavily depends on the self attention, which is usually inaccurate in the early training stage. Moreover, in a scene of dense objects, the object query interacts with many irrelevant ones, reducing its uniqueness and harming the performance. This paper proposes to use IoU between different boxes as a prior for the value routing in self attention. The original attention matrix multiplies the same size matrix computed from the IoU of proposal boxes, and they determine the routing scheme so that the irrelevant features can be suppressed. Furthermore, to accurately extract features for both classification and regression, we add two lightweight projection heads to provide the dynamic channel masks based on object query, and they multiply with the output from dynamic convs, making the results suitable for the two different tasks. We validate the proposed scheme on different datasets, including MS-COCO and CrowdHuman, showing that it significantly improves the performance and increases the model convergence speed. Codes are available at https://***/bravezzzzzz/IoU-Enhanced-Attention. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

A majorized-generalized alternating direction method of multipliers for convex composite programming

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Qin, Congying Xiao, Yunhai Li, Peili School of Mathematics and Statistics Henan University Kaifeng475000 China Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms Henan University Kaifeng475000 China School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai200062 China

The linearly constrained convex composite programming problems whose objective function contains two blocks with each block being the form of nonsmooth+smooth arises frequently in multiple fields of applications. If both of the smooth terms are quadratic, this problem can be solved efficiently by using the symmetric Gaussian-Seidel (sGS) technique based proximal alternating direction method of multipliers (ADMM). However, in the non-quadratic case, the sGS technique can not be used any more, which leads to the separable structure of nonsmooth+smooth had to be ignored. In this paper, we present a generalized ADMM and particularly use a majorization technique to make the corresponding subproblems more amenable to efficient computations. Under some appropriate conditions, we prove its global convergence for the relaxation factor in (0, 2). We apply the algorithm to solve a kind of simulated convex composite optimization problems and a type of sparse inverse covariance matrix estimation problems which illustrates that the effectiveness of the algorithm are obvious. Copyright © 2021, The Authors. All rights reserved.

关键词： Covariance matrix

OD-DETR: Online Distillation for Stabilizing Training of Detection Transformer

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wu, Shengjian Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China FinVolution Group Iran Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University China

DEtection TRansformer (DETR) becomes a dominant paradigm, mainly due to its common architecture with high accuracy and no post-processing. However, DETR suffers from unstable training dynamics. It consumes more data and epochs to converge compared with CNN-based detectors. This paper aims to stabilize DETR training through the online distillation. It utilizes a teacher model, accumulated by Exponential Moving Average (EMA), and distills its knowledge into the online model in following three aspects. First, the matching relation between object queries and ground truth (GT) boxes in the teacher is employed to guide the student, so queries within the student are not only assigned labels based on their own predictions, but also refer to the matching results from the teacher. Second, the teacher’s initial query is given to the online student, and its prediction is directly constrained by the corresponding output from the teacher. Finally, the object queries from teacher’s different decoder stages are used to build the auxiliary group to accelerate the convergence. For each GT, two queries with the least matching costs are selected into this extra group, and they predict the GT box and participate the optimization. Extensive experiments show that the proposed OD-DETR successfully stabilizes the training, and significantly increases the performance without bringing in more parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

Time-Consistent Portfolio Selection for Rank-Dependent Utilities in an Incomplete Market

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wei, Jiaqin Xia, Jianming Zhao, Qian Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai200062 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Statistics and Information Shanghai University of International Business and Economics Shanghai201620 China

We investigate the portfolio selection problem for an agent with rank-dependent utility in an incomplete financial market. For a constant-coefficient market and CRRA utilities, we characterize the deterministic strict equilibrium strategies. In the case of time-invariant probability weighting function, we provide a comprehensive characterization of the deterministic strict equilibrium strategy. The unique non-zero equilibrium, if exists, can be determined by solving an autonomous ODE. In the case of time-variant probability weighting functions, we observe that there may be infinitely many non-zero deterministic strict equilibrium strategies, which are derived from the positive solutions to a nonlinear singular ODE. By specifying the maximal solution to the singular ODE, we are able to identify all the positive solutions. In addition, we address the issue of selecting an optimal strategy from the numerous equilibrium strategies available. © 2024, CC BY-NC-ND.

关键词： Commerce

An efficient semismooth Newton method for adaptive sparse signal recovery problems

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Ding, Yanyun Zhang, Haibin Li, Peili Xiao, Yunhai Department of Operations Research and Information Engineering Beijing University of Technology Beijing100124 China 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 Henan University Kaifeng475000 China

We know that compressive sensing can establish stable sparse recovery results from highly undersampled data under a restricted isometry property condition. In reality, however, numerous problems are coherent, and vast majority conventional methods might work not so well. Recently, it was shown that using the difference between l1- and l2-norm as a regularization always has superior performance. In this paper, we propose an adaptive lp-l1−2 model where the lp-norm with p ≥ 1 measures the data fidelity and the l1−2-term measures the sparsity. This proposed model has the ability to deal with different types of noises and extract the sparse property even under high coherent condition. We use a proximal majorization-minimization technique to handle the nonconvex regularization term and then employ a semismooth Newton method to solve the corresponding convex relaxation subproblem. We prove that the sequence generated by the semismooth Newton method admits fast local convergence rate to the subproblem under some technical assumptions. Finally, we do some numerical experiments to demonstrate the superiority of the proposed model and the progressiveness of the proposed algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressed sensing

Variable screening in multivariate linear regression with high-dimensional covariates

学校读者我要写书评

暂无评论

Statistical theory and Related Fields 2022年第3期6卷 241-253页

作者： Shiferaw B.Bizuayehu Lu Li Jin Xu School of Statistics East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal UniversityShanghaiPeople’s Republic of China

We propose two variable selection methods in multivariate linear regression with highdimensional *** first method uses a multiple correlation coefficient to fast reduce the dimension of the relevant predictors to a moderate or low *** second method extends the univariate forward regression of Wang[(2009).Forward regression for ultra-high dimensional variable *** of the American Statistical Association,104(488),1512–***://***/10.1198/***08516]in a unified way such that the variable selection and model estimation can be obtained *** establish the sure screening property for both *** and real data applications are presented to show the finite sample performance of the proposed methods in comparison with some naive method.

关键词： Dimension reduction forward regression multiple correlation coefficient multivariate regression variable selection