检索结果-内蒙古大学图书馆

Conservative Policy Construction Using variational autoencoders for Logged Data With Missing Values

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 6368-6378页

作者： Abroshan, Mahed Yip, Kai Hou Tekin, Cem van der Schaar, Mihaela Alan Turing Inst London NW1 2DB England UCL London WC1E 6BT England Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey Univ Cambridge Dept Appl Math & Theoret Phys Cambridge CB2 1TN England Univ Calif Los Angeles Los Angeles CA 90095 USA

In high-stakes applications of data-driven decision-making such as healthcare, it is of paramount importance to learn a policy that maximizes the reward while avoiding potentially dangerous actions when there is uncertainty. There are two main challenges usually associated with this problem. First, learning through online exploration is not possible due to the critical nature of such applications. Therefore, we need to resort to observational datasets with no counterfactuals. Second, such datasets are usually imperfect, additionally cursed with missing values in the attributes of features. In this article, we consider the problem of constructing personalized policies using logged data when there are missing values in the attributes of features in both training and test data. The goal is to recommend an action (treatment) when (X) over tilde, a degraded version of X with missing values, is observed. We consider three strategies for dealing with missingness. In particular, we introduce the conservative strategy where the policy is designed to safely handle the uncertainty due to missingness. In order to implement this strategy, we need to estimate posterior distribution p(X vertical bar(X) over tilde) and use a variational autoencoder to achieve this. In particular, our method is based on partial variational autoencoders (PVAEs) that are designed to capture the underlying structure of features with missing values.

关键词： Missing values observational data policy construction variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Mitigating the Impact of Temperature Variations on Ultrasonic Guided Wave-Based Structural Health Monitoring through variational autoencoders

引用

SENSORS 2024年第5期24卷 1494页

作者： Junges, Rafael Lomazzi, Luca Miele, Lorenzo Giglio, Marco Cadini, Francesco Politecn Milan Dept Mech Engn Via Masa n1 I-20156 Milan Italy

Structural health monitoring (SHM) has become paramount for developing cheaper and more reliable maintenance policies. The advantages coming from adopting such process have turned out to be particularly evident when dealing with plated structures. In this context, state-of-the-art methods are based on exciting and acquiring ultrasonic-guided waves through a permanently installed sensor network. A baseline is registered when the structure is healthy, and newly acquired signals are compared to it to detect, localize, and quantify damage. To this purpose, the performance of traditional methods has been overcome by data-driven approaches, which allow processing a larger amount of data without losing diagnostic information. However, to date, no diagnostic method can deal with varying environmental and operational conditions (EOCs). This work aims to present a proof-of-concept that state-of-the-art machine learning methods can be used for reducing the impact of EOCs on the performance of damage diagnosis methods. Generative artificial intelligence was leveraged to mitigate the impact of temperature variations on ultrasonic guided wave-based SHM. Specifically, variational autoencoders and singular value decomposition were combined to learn the influence of temperature on guided waves. After training, the generative part of the algorithm was used to reconstruct signals at new unseen temperatures. Moreover, a refined version of the algorithm called forced variational autoencoder was introduced to further improve the reconstruction capabilities. The accuracy of the proposed framework was demonstrated against real measurements on a composite plate.

关键词： generative artificial intelligence variational autoencoder temperature ultrasonic guided wave

来源：评论

学校读者我要写书评

暂无评论

Using Shapley Values and variational autoencoders to Explain Predictive Models with Dependent Mixed Features

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-51页

作者： Olsen, Lars Henry Berge Glad, Ingrid Kristine Jullum, Martin Aas, Kjersti Univ Oslo Dept Math Moltke Moes vei 35Niels Henrik Abels hus N-0851 Oslo Norway Norwegian Comp Ctr Gaustadalleen 23aKristen Nygaards hus N-0373 Oslo Norway

Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoret-ical properties and a sound mathematical foundation in the field of cooperative game theory. Precise Shapley value estimates for dependent data rely on accurate modeling of the depen-dencies between all feature combinations. In this paper, we use a variational autoencoder with arbitrary conditioning (VAEAC) to model all feature dependencies simultaneously. We demonstrate through comprehensive simulation studies that our VAEAC approach to Shapley value estimation outperforms the state-of-the-art methods for a wide range of settings for both continuous and mixed dependent features. For high-dimensional settings, our VAEAC approach with a non-uniform masking scheme significantly outperforms competing meth-ods. Finally, we apply our VAEAC approach to estimate Shapley value explanations for the Abalone data set from the UCI Machine Learning Repository.

关键词： Explainable artificial intelligence prediction explanation Shapley values variational autoencoder feature dependence

来源：评论

学校读者我要写书评

暂无评论

Recurrent variational autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers

引用

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2018年第6期12卷 1615-1627页

作者： Wang, Yu Dai, Bin Hua, Gang Aston, John Wipf, David Univ Cambridge Dept Pure Math & Stat Cambridge CB2 1TN England Tsinghua Univ Beijing 100084 Peoples R China Microsoft Res Redmond WA 98052 USA

This paper explores two useful modifications of the recent variational autoencoder (VAE), a popular deep generative modeling framework that dresses traditional autoencoders with probabilistic attire. The first involves a specially-tailored form of conditioning that allows us to simplify the VAE decoder structure while simultaneously introducing robustness to outliers. In a related vein, a second, complementary alteration is proposed to further build invariance to contaminated or dirty samples via a data augmentation process that amounts to recycling. In brief, to the extent that the VAE is legitimately a representative generative model, then each output from the decoder should closely resemble an authentic sample, which can then be resubmitted as a novel input ad infinitum. Moreover, this can be accomplished via special recurrent connections without the need for additional parameters to be trained. We evaluate these proposals on multiple practical outlier-removal and generative modeling tasks involving nonlinear low-dimensional manifolds, demonstrating considerable improvements over existing algorithms.

关键词： Deep generative models variational autoencoder robust PCA outlier removal variational Bayesian model deep learning

来源：评论

学校读者我要写书评

暂无评论

A process monitoring and fault isolation framework based on variational autoencoders and branch and bound method

引用

JOURNAL OF THE FRANKLIN INSTITUTE 2022年第2期359卷 1667-1691页

作者： Tang, Peng Peng, Kaixiang Jiao, Ruihua Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Peng Cheng Lab Dept Math & Theories 2 Xingke 1st St Nanshan Shenzhen Peoples R China AVIC Xian Aviat Brake Technol Co Ltd Xian 710065 Peoples R China

Nonlinear characteristic widely exists in industrial processes. Many approaches based on kernel methods and machine learning have been developed for nonlinear process monitoring. However, the fault isolation for nonlinear processes has rarely been studied in previous works. In this paper, a process monitoring and fault isolation framework is proposed for nonlinear processes using variational autoen-coder (VAE) model. First, based on the probability graph model of VAE, a uniform monitoring index can be calculated by the probability density of observation variables. Then, the fault variables are estimated with normal variables by a missing value estimation method. The optimal fault variable set can be searched by branch and bound (BAB) algorithm. The proposed method can resolve the "smearing effects" problem existing in traditional fault isolation methods. Finally, a numerical case and a hot strip mill process case are used to verified the proposed method. (C) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Process monitoring Fault isolation variational autoencoder Missing value estimation Branch and bound

来源：评论

学校读者我要写书评

暂无评论

Leveraging variational autoencoders for Parameterized MMSE Estimation

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 3731-3744页

作者： Baur, Michael Fesl, Benedikt Utschick, Wolfgang Tech Univ Munich TUM Sch Computat Informat & Technol D-80333 Munich Germany

In this manuscript, we propose to use a variational autoencoder-based framework for parameterizing a conditional linear minimum mean squared error estimator. The variational autoencoder models the underlying unknown data distribution as conditionally Gaussian, yielding the conditional first and second moments of the estimand, given a noisy observation. The derived estimator is shown to approximate the minimum mean squared error estimator by utilizing the variational autoencoder as a generative prior for the estimation problem. We propose three estimator variants that differ in their access to ground-truth data during the training and estimation phases. The proposed estimator variant trained solely on noisy observations is particularly noteworthy as it does not require access to ground-truth data during training or estimation. We conduct a rigorous analysis by bounding the difference between the proposed and the minimum mean squared error estimator, connecting the training objective and the resulting estimation performance. Furthermore, the resulting bound reveals that the proposed estimator entails a bias-variance tradeoff, which is well-known in the estimation literature. As an example application, we portray channel estimation, allowing for a structured covariance matrix parameterization and low-complexity implementation. Nevertheless, the proposed framework is not limited to channel estimation but can be applied to a broad class of estimation problems. Extensive numerical simulations first validate the theoretical analysis of the proposed variational autoencoder-based estimators and then demonstrate excellent estimation performance compared to related classical and machine learning-based state-of-the-art estimators.

关键词： Estimation Training Inverse problems Data models Bayes methods Noise measurement Vectors Parameter estimation variational autoencoder conditional mean estimator generative model inverse problem

来源：评论

学校读者我要写书评

暂无评论

Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation

引用

JOURNAL OF COMPUTATIONAL PHYSICS 2024年 515卷

作者： Glyn-Davies, Alex Duffin, Connor Akyildiz, O. Deniz Girolami, Mark Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England Imperial Coll London Dept Math Exhibit Rd London SW7 2AZ England Alan Turing Inst British Lib 96 Euston Rd London NW1 2DB England

Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operators whose functional forms are typically assumed to be known. This prevents these methods from achieving a consistent model-data synthesis in configurations where the mapping from data-space to model- space is unknown. To address these shortcomings, in this paper we develop a physics-informed dynamical variational autoencoder (Phi-DVAE) to embed diverse data streams into time-evolving physical systems described by differential equations. Our approach combines a standard, possibly nonlinear, filter for the latent state-space model and a VAE, to assimilate the unstructured data into the latent dynamical system. Unstructured data, in our example systems, comes in the form of video data and velocity field measurements, however the methodology is suitably generic to allow for arbitrary unknown observation operators. A variational Bayesian framework is used for the joint estimation of the encoding, latent states, and unknown system parameters. To demonstrate the method, we provide case studies with the Lorenz-63 ordinary differential equation, and the advection and Korteweg-de Vries partial differential equations. Our results, with synthetic data, show that Phi-DVAE provides a data efficient dynamics encoding methodology which is competitive with standard approaches. Unknown parameters are recovered with uncertainty quantification, and unseen data are accurately predicted.

关键词： Data assimilation Nonlinear filtering variational autoencoder Inverse problem variational Bayes

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Design of DNA-Stabilized Nanoclusters Using variational autoencoders With Automatic Feature Extraction

引用

ACS NANO 2024年第39期18卷 26997-27008页

作者： Sadeghi, Elham Mastracco, Peter Gonzalez-Rosell, Anna Copp, Stacy M. Bogdanov, Petko Univ Albany SUNY Dept Comp Sci Albany NY 12222 USA Univ Calif Irvine Dept Mat Sci & Engn Irvine CA 92697 USA Univ Calif Irvine Dept Chem Dept Chem & Biomol Engn Irvine CA 92697 USA Univ Calif Irvine Dept Phys & Astron Irvine CA 92697 USA

DNA-stabilized silver nanoclusters (Ag-N-DNAs) have sequence-tuned compositions and fluorescence colors. High-throughput experiments together with supervised machine learning models have recently enabled design of DNA templates that select for Ag-N-DNA properties, including near-infrared (NIR) emission that holds promise for deep tissue bioimaging. However, these existing models do not enable simultaneous selection of multiple Ag-N-DNA properties, and require significant expert input for feature engineering and class definitions. This work presents a model for multiobjective, continuous-property design of Ag-N-DNAs with automatic feature extraction, based on variational autoencoders (VAEs). This model is generative, i.e., it learns both the forward mapping from DNA sequence to Ag-N-DNA properties and the inverse mapping from properties to sequence, and is trained on an experimental data set of DNA sequences paired with Ag-N-DNA fluorescence properties. Experimental testing shows that the model enables effective design of Ag-N-DNA emission, including bright NIR Ag-N-DNAs with 4-fold greater abundance compared to training data. In addition, Shapley analysis is employed to discern learned nucleobase patterns that correspond to fluorescence color and brightness. This generative model can be adapted for a range of biomolecular systems with sequence-dependent properties, enabling precise design of emerging biomolecular nanomaterials.

关键词： variational autoencoder silver nanocluster DNA interpretable machinelearning fluorescence near-infrared

来源：评论

学校读者我要写书评

暂无评论

Disease Progression Score Estimation From Multimodal Imaging and MicroRNA Data Using Supervised variational autoencoders

引用

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第12期26卷 6024-6035页

作者： Kmetzsch, Virgilio Becker, Emmanuelle Saracino, Dario Rinaldi, Daisy Camuzat, Agnes Le Ber, Isabelle Colliot, Olivier Sorbonne Univ Paris Brain Inst ICM AP HPCNRSInriaInsermInst Cerveau F-75013 Paris France Univ Rennes Inria CNRS IRISA F-35000 Rennes France Sorbonne Univ Paris Brain Inst ICM AP HPInst CerveauCNRSInserm F-75013 Paris France Sorbonne Univ Paris Brain Inst Hop Pitie Salpetriere AP HPInst Memory & Alzheimers Dis IM2AInst Cerv F-75013 Paris France

Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers allowing an accurate assessment of disease progression is crucial for evaluating new therapies. Concretely, neuroimaging and transcriptomic (microRNA) data have been shown useful in tracking their progression. However, no single biomarker can accurately measure progression in these complex diseases. Additionally, large samples are not available for such rare disorders. It is thus essential to develop methods that can model disease progression by combining multiple biomarkers from small samples. In this paper, we propose a new framework for computing a disease progression score (DPS) from cross-sectional multimodal data. Specifically, we introduce a supervised multimodal variational autoencoder that can infer a meaningful latent space, where latent representations are placed along a disease trajectory. A score is computed by orthogonal projections onto this path. We evaluate our framework with multiple synthetic datasets and with a real dataset containing 14 patients, 40 presymptomatic genetic mutation carriers and 37 controls from the PREV-DEMALS study. There is no ground truth for the DPS in real-world scenarios, therefore we use the area under the ROC curve (AUC) as a proxy metric. Results with the synthetic datasets support this choice, since the higher the AUC, the more accurate the predicted simulated DPS. Experiments with the real dataset demonstrate better performance in comparison with state-of-the-art approaches. The proposed framework thus leverages cross-sectional multimodal datasets with small sample sizes to objectively measure disease progression, with potential application in clinical trials.

关键词： Disease progression score deep learning microRNA multimodal data neurodegenerative disease neuroimaging variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Convolutional variational autoencoders and Resampling Techniques with Generative Adversarial Network for Enhancing Internet of Thing Security

引用

PATTERN RECOGNITION AND IMAGE ANALYSIS 2024年第3期34卷 562-569页

作者： Dong, Huiyao Kotenko, I. V. Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg 199178 Russia

The Internet of Things is a pivotal constituent of the contemporary technological revolution and has experienced expeditious expansion in recent times. The proliferation of Internet of Things devices has led to enhanced convenience and automation. However, the extensive deployment of Internet of Things devices has also engendered concerns regarding data privacy and security. Among various detection and prevention methodologies, deep learning is emerging as a prominent trend. This paper ultilizes convolutional variational autoencoders and resampling techniques for network attacks detection. The proposed methodology employs a hybrid data resampling technique to tackle the issue of imbalanced classes, followed by the implementation of a convolutional variational autoencoder classification model with a weighted loss function. The experiments demonstrate that the light-weighted convolutional variational autoencoder outperforms the baseline models. Therefore, it possesses the capability to effectively detect intrusive activities in real-world settings and strengthen the Internet of Things security.

关键词： Internet of Things cyber-attacks variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：