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Foundations of data science (Springfield, Mo.)最新文献

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Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators 基于独立EnKF估计的样本平均值的多水平集成卡尔曼滤波
Q2 Mathematics Pub Date : 2020-02-02 DOI: 10.3934/fods.2020017
Håkon Hoel, G. Shaimerdenova, R. Tempone
We introduce a new multilevel ensemble Kalman filter method (MLEnKF) which consists of a hierarchy of independent samples of ensemble Kalman filters (EnKF). This new MLEnKF method is fundamentally different from the preexisting method introduced by Hoel, Law and Tempone in 2016, and it is suitable for extensions towards multi-index Monte Carlo based filtering methods. Robust theoretical analysis and supporting numerical examples show that under appropriate regularity assumptions, the MLEnKF method has better complexity than plain vanilla EnKF in the large-ensemble and fine-resolution limits, for weak approximations of quantities of interest. The method is developed for discrete-time filtering problems with finite-dimensional state space and linear observations polluted by additive Gaussian noise.
我们介绍了一种新的多级集成卡尔曼滤波器方法(MLEnKF),该方法由集成卡尔曼滤波器的独立样本层次组成。这种新的MLEnKF方法与Hoel、Law和Tempone在2016年引入的现有方法有根本不同,它适用于向基于多指标蒙特卡罗的滤波方法扩展。稳健的理论分析和支持的数值例子表明,在适当的正则性假设下,对于感兴趣的量的弱近似,MLEnKF方法在大系综和精细分辨率极限方面比普通EnKF具有更好的复杂性。该方法是针对有限维状态空间和线性观测受到加性高斯噪声污染的离散时间滤波问题而开发的。
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引用次数: 13
Index 指数
Q2 Mathematics Pub Date : 2020-01-31 DOI: 10.1017/9781108755528.013
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引用次数: 0
Introduction 介绍
Q2 Mathematics Pub Date : 2020-01-31 DOI: 10.1017/9781108755528.001
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引用次数: 0
High-Dimensional Space 高维空间
Q2 Mathematics Pub Date : 2020-01-31 DOI: 10.1017/9781108755528.002
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引用次数: 4
Topic Models, Nonnegative Matrix Factorization, Hidden Markov Models, and Graphical Models 主题模型,非负矩阵分解,隐马尔可夫模型和图形模型
Q2 Mathematics Pub Date : 2020-01-31 DOI: 10.1017/9781108755528.009
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引用次数: 0
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization 融合数据同化、机器学习和期望最大化的混沌动力学贝叶斯推理
Q2 Mathematics Pub Date : 2020-01-17 DOI: 10.3934/fods.2020004
M. Bocquet, J. Brajard, A. Carrassi, Laurent Bertino
The reconstruction from observations of high-dimensional chaotic dynamics such as geophysical flows is hampered by (i) the partial and noisy observations that can realistically be obtained, (ii) the need to learn from long time series of data, and (iii) the unstable nature of the dynamics. To achieve such inference from the observations over long time series, it has been suggested to combine data assimilation and machine learning in several ways. We show how to unify these approaches from a Bayesian perspective using expectation-maximization and coordinate descents. In doing so, the model, the state trajectory and model error statistics are estimated all together. Implementations and approximations of these methods are discussed. Finally, we numerically and successfully test the approach on two relevant low-order chaotic models with distinct identifiability.
从高维混沌动力学(如地球物理流)的观测重建受到以下阻碍:(i)可以实际获得的部分和有噪声的观测,(ii)需要从长时间序列的数据中学习,以及(iii)动力学的不稳定性质。为了从长时间序列的观测中实现这种推断,有人建议以几种方式将数据同化和机器学习相结合。我们展示了如何从贝叶斯的角度使用期望最大化和坐标下降来统一这些方法。在这样做的过程中,模型、状态轨迹和模型误差统计信息被一起估计。讨论了这些方法的实现和近似。最后,我们在两个具有不同可识别性的相关低阶混沌模型上成功地对该方法进行了数值测试。
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引用次数: 75
Mean-field and kinetic descriptions of neural differential equations 神经微分方程的平均场和动力学描述
Q2 Mathematics Pub Date : 2020-01-07 DOI: 10.3934/fods.2022007
M. Herty, T. Trimborn, G. Visconti
Nowadays, neural networks are widely used in many applications as artificial intelligence models for learning tasks. Since typically neural networks process a very large amount of data, it is convenient to formulate them within the mean-field and kinetic theory. In this work we focus on a particular class of neural networks, i.e. the residual neural networks, assuming that each layer is characterized by the same number of neurons begin{document}$ N $end{document}, which is fixed by the dimension of the data. This assumption allows to interpret the residual neural network as a time-discretized ordinary differential equation, in analogy with neural differential equations. The mean-field description is then obtained in the limit of infinitely many input data. This leads to a Vlasov-type partial differential equation which describes the evolution of the distribution of the input data. We analyze steady states and sensitivity with respect to the parameters of the network, namely the weights and the bias. In the simple setting of a linear activation function and one-dimensional input data, the study of the moments provides insights on the choice of the parameters of the network. Furthermore, a modification of the microscopic dynamics, inspired by stochastic residual neural networks, leads to a Fokker-Planck formulation of the network, in which the concept of network training is replaced by the task of fitting distributions. The performed analysis is validated by artificial numerical simulations. In particular, results on classification and regression problems are presented.
如今,神经网络作为学习任务的人工智能模型被广泛应用于许多应用中。由于神经网络通常处理大量数据,因此在平均场和动力学理论中对其进行公式化是很方便的。在这项工作中,我们专注于一类特定的神经网络,即残差神经网络,假设每一层都由相同数量的神经元开始{文档}$N$结束{文档}表征,这是由数据的维度固定的。这一假设允许将残差神经网络解释为时间离散常微分方程,类似于神经微分方程。然后在无限多个输入数据的限制下获得平均场描述。这导致了描述输入数据分布演变的Vlasov型偏微分方程。我们分析了网络参数的稳态和灵敏度,即权重和偏差。在线性激活函数和一维输入数据的简单设置中,矩的研究为网络参数的选择提供了见解。此外,受随机残差神经网络的启发,对微观动力学进行了修改,得出了网络的福克-普朗克公式,其中网络训练的概念被拟合分布的任务所取代。通过人工数值模拟验证了所进行的分析。特别地,给出了关于分类和回归问题的结果。
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引用次数: 4
Topological reconstruction of sub-cellular motion with Ensemble Kalman velocimetry 基于集合卡尔曼速度法的亚细胞运动拓扑重建
Q2 Mathematics Pub Date : 2020-01-01 DOI: 10.3934/fods.2020007
Le Yin, Ioannis Sgouralis, V. Maroulas
Microscopy imaging of plant cells allows the elaborate analysis of sub-cellular motions of organelles. The large video data set can be efficiently analyzed by automated algorithms. We develop a novel, data-oriented algorithm, which can track organelle movements and reconstruct their trajectories on stacks of image data. Our method proceeds with three steps: (ⅰ) identification, (ⅱ) localization, and (ⅲ) linking. This method combines topological data analysis and Ensemble Kalman Filtering, and does not assume a specific motion model. Application of this method on simulated data sets shows an agreement with ground truth. We also successfully test our method on real microscopy data.
植物细胞的显微镜成像可以详细分析细胞器的亚细胞运动。自动化算法可以有效地分析大型视频数据集。我们开发了一种新颖的,面向数据的算法,它可以跟踪细胞器运动并在图像数据堆栈上重建它们的轨迹。我们的方法分为三个步骤:(ⅰ)识别,(ⅱ)定位,(ⅲ)连接。该方法结合了拓扑数据分析和集成卡尔曼滤波,不假设特定的运动模型。在模拟数据集上的应用表明,该方法与地面真实值一致。我们还成功地在真实的显微镜数据上测试了我们的方法。
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引用次数: 0
Stochastic gradient descent algorithm for stochastic optimization in solving analytic continuation problems 求解解析延拓问题的随机优化的随机梯度下降算法
Q2 Mathematics Pub Date : 2020-01-01 DOI: 10.3934/fods.2020001
F. Bao, T. Maier
We propose a stochastic gradient descent based optimization algorithm to solve the analytic continuation problem in which we extract real frequency spectra from imaginary time Quantum Monte Carlo data. The procedure of analytic continuation is an ill-posed inverse problem which is usually solved by regularized optimization methods, such like the Maximum Entropy method, or stochastic optimization methods. The main contribution of this work is to improve the performance of stochastic optimization approaches by introducing a supervised stochastic gradient descent algorithm to solve a flipped inverse system which processes the random solutions obtained by a type of Fast and Efficient Stochastic Optimization Method.
针对从虚时间量子蒙特卡罗数据中提取实频谱的解析延拓问题,提出了一种基于随机梯度下降的优化算法。解析延拓过程是一个病态逆问题,通常用正则化优化方法求解,如最大熵法或随机优化方法。本文的主要贡献是通过引入有监督的随机梯度下降算法来求解翻转逆系统,从而提高随机优化方法的性能,该算法处理由一种快速有效的随机优化方法得到的随机解。
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引用次数: 6
Hierarchical approximations for data reduction and learning at multiple scales 多尺度下数据约简和学习的层次近似
Q2 Mathematics Pub Date : 2020-01-01 DOI: 10.3934/fods.2020008
P. Shekhar, A. Patra
This paper describes a hierarchical learning strategy for generating sparse representations of multivariate datasets. The hierarchy arises from approximation spaces considered at successively finer scales. A detailed analysis of stability, convergence and behavior of error functionals associated with the approximations are presented, along with a well chosen set of applications. Results show the performance of the approach as a data reduction mechanism for both synthetic (univariate and multivariate) and a real dataset (geo-spatial). The sparse representation generated is shown to efficiently reconstruct data and minimize error in prediction. The approach is also shown to generalize well to unseen samples, extending its prospective application to statistical learning problems.
本文描述了一种用于生成多元数据集稀疏表示的分层学习策略。层次结构产生于在连续更细尺度上考虑的近似空间。详细分析了与近似相关的误差函数的稳定性、收敛性和行为,以及一组精心选择的应用。结果表明,该方法既适用于合成数据集(单变量和多变量),也适用于真实数据集(地理空间)。所生成的稀疏表示可以有效地重构数据并将预测误差降至最低。该方法也被证明可以很好地推广到看不见的样本,将其应用于统计学习问题。
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引用次数: 6
期刊
Foundations of data science (Springfield, Mo.)
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