检索结果-内蒙古大学图书馆

VAPER: A deep learning model for explainable probabilistic regression

JOURNAL OF COMPUTATIONAL SCIENCE 2022年第0期63卷

作者： Jung, Seungwon Noh, Yoona Moon, Jaeuk Hwang, Eenjun Korea Univ Sch Elect Engn Seoul 02841 South Korea

A probabilistic regression model provides decision-makers with the regression output along with its quantitative uncertainty for given input variables. Even though this uncertainty may help to avoid serious consequences such as misdiagnosis or blackout due to overconfidence in the output, it only provides a measure of the uncertainty of the output, and has a limitation in that it cannot explain the reasons for the output and its uncertainty. If they can be presented along with their reasons, more suitable alternatives to the output can be found. However, despite the development of artificial intelligence methods to explain machine learning models and their outputs, few probabilistic regression models with this functionality have been proposed. Therefore, in this paper, we propose a variational autoencoder-based model for explainable probabilistic regression, called VAPER. VAPER provides a parametric probability distribution of an output variable over input variables and interprets it using layer-wise relevance propagation to investigate the effect of each input variable. To evaluate the effectiveness of the pro-posed model, we performed extensive experiments using several datasets. The experimental results demonstrated that VAPER has competitive regression performance compared to existing models, even with effective explainability.

关键词： Probabilistic regression Explainable artificial intelligence Layer -wise relevance propagation variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

A review of molecular representation in the age of machine learning

引用

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2022年第5期12卷 e1603-e1603页

作者： Wigh, Daniel S. Goodman, Jonathan M. Lapkin, Alexei A. Univ Cambridge Dept Chem Engn & Biotechnol Cambridge CB3 0AS England Univ Cambridge Yusuf Hamied Dept Chem Cambridge England

Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation of molecules in a machine-readable format, as this is central to computational chemistry. Four classes of representations are introduced: string, connection table, feature-based, and computer-learned representations. Three of the most significant representations are simplified molecular-input line-entry system (SMILES), International Chemical Identifier (InChI), and the MDL molfile, of which SMILES was the first to successfully be used in conjunction with a variational autoencoder (VAE) to yield a continuous representation of molecules. This is noteworthy because a continuous representation allows for efficient navigation of the immensely large chemical space of possible molecules. Since 2018, when the first model of this type was published, considerable effort has been put into developing novel and improved methodologies. Most, if not all, researchers in the community make their work easily accessible on GitHub, though discussion of computation time and domain of applicability is often overlooked. Herein, we present questions for consideration in future work which we believe will make chemical VAEs even more accessible. This article is categorized under: Data Science > Chemoinformatics

关键词： chemoinformatics fingerprints machine learning molecular representation variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Driver Intent-Based Intersection Autonomous Driving Collision Avoidance Reinforcement Learning Algorithm

引用

SENSORS 2022年第24期22卷 9943-9943页

作者： Chen, Ting Chen, Youjing Li, Hao Gao, Tao Tu, Huizhao Li, Siyu Changan Univ Sch Informat Engn Xian 710064 Peoples R China Tongji Univ Coll Transportat Engn Key Lab Rd Traff Engn Minist Educ Shanghai 201804 Peoples R China

With the rapid development of artificial intelligent technology, the deep learning method is widely applied to predict human driving intentions due to its relative accuracy of prediction, which is one of critical links for security guarantee in the distributed, mixed driving scenario. In order to sense the intention of human-driven vehicles and reduce the self-driving collision avoidance rate, an improved intention prediction method for human-driving vehicles based on unsupervised, deep inverse reinforcement learning is proposed. Firstly, a contrast discriminator module was proposed to extract richer features. Then, the residual module was created to overcome the drawbacks of gradient disappearance and network degradation with the increase in network layers. Furthermore, the dropout layer was generated to prevent the over-fitting phenomenon in the whole training process of the GRU network, so as to improve the generalization ability of the network model. Finally, abundant experiments were conducted on datasets to evaluate our proposed method. The pass rate of self-driving vehicles with conservative driver probabilities of p = 0.25, p = 0.4, and p = 0.6 improved by a maximum of 8%, 10%, and 3%, compared with the classical method LSTM and VAE + RNN. It indicates that the prediction results of our proposed method fit more with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method.

关键词： self-driving vehicles latent states variational autoencoder deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

EfficientFi: Toward Large-Scale Lightweight WiFi Sensing via CSI Compression

引用

IEEE INTERNET OF THINGS JOURNAL 2022年第15期9卷 13086-13095页

作者： Yang, Jianfei Chen, Xinyan Zou, Han Wang, Dazhuo Xu, Qianwen Xie, Lihua Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA KTH Royal Inst Technol Dept Elect Power & Energy Syst S-10044 Stockholm Sweden

WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since it is device free, cost effective and privacy preserving. Though numerous WiFi sensing methods have been developed, most of them only consider single smart home scenario. Without the connection of powerful cloud server and massive users, large-scale WiFi sensing is still difficult. In this article, we first analyze and summarize these obstacles, and propose an efficient large-scale WiFi sensing framework, namely, EfficientFi. The EfficientFi works with edge computing at WiFi access points and cloud computing at center servers. It consists of a novel deep neural network that can compress fine-grained WiFi channel state information (CSI) at edge, restore CSI at cloud, and perform sensing tasks simultaneously. A quantized autoencoder and a joint classifier are designed to achieve these goals in an end-to-end fashion. To the best of our knowledge, the EfficientFi is the first Internet of Things-cloud-enabled WiFi sensing framework that significantly reduces communication overhead while realizing sensing tasks accurately. We utilized human activity recognition (HAR) and identification via WiFi sensing as two case studies, and conduct extensive experiments to evaluate the EfficientFi. The results show that it compresses CSI data from 1.368 Mb/s to 0.768 kb/s with extremely low error of data reconstruction and achieves over 98% accuracy for HAR.

关键词： Sensors Wireless fidelity Servers Cloud computing Feature extraction Internet of Things Deep learning Channel state information (CSI) deep neural network discrete representation learning multitask learning variational autoencoder WiFi-based sensing

来源：评论

学校读者我要写书评

暂无评论

Subtle anomaly detection: Application to brain MRI analysis of de novo Parkinsonian patients

引用

ARTIFICIAL INTELLIGENCE IN MEDICINE 2022年 125卷 102251-102251页

作者： Munoz-Ramirez, Veronica Kmetzsch, Virgilio Forbes, Florence Meoni, Sara Moro, Elena Dojat, Michel Univ Grenoble Alpes INRIA Grenoble INP CNRSLJK F-38000 Grenoble France Univ Grenoble Alpes Inserm U1216 CHU Grenoble Alpes Grenoble Inst Neurosci F-38000 Grenoble France CHU Grenoble Alpes Div Neurol F-38000 Grenoble France

With the advent of recent deep learning techniques, computerized methods for automatic lesion segmentation have reached performances comparable to those of medical practitioners. However, little attention has been paid to the detection of subtle physiological changes caused by evolutive pathologies, such as neurodegenerative diseases. In this work, we leverage deep learning models to detect anomalies in brain diffusion tensor imaging (DTI) parameter maps of recently diagnosed and untreated (de novo) patients with Parkinson's disease (PD). For this purpose, we trained auto-encoders on parameter maps of healthy controls (n = 56) and tested them on those of de novo PD patients (n = 129). We considered large reconstruction errors between the original and reconstructed images to be anomalies that, when quantified, allow discerning between de novo PD patients and healthy controls. The most discriminating brain macro-region was found to be the white matter with a ROC-AUC 68.3 (IQR 5.4) and the best subcortical structure, the GPi (ROC-AUC 62.6 IQR 5.4). Our results indicate that our deep learning-based model can detect potentially pathological regions in de novo PD patients, without requiring any expert delineation. This may enable extracting neuroimaging biomarkers of PD in the future, but further testing on larger cohorts is needed. Such models can be seamlessly extended with additional parameter maps and applied to study the physio-pathology of other neurological diseases.

关键词： Machine learning Unsupervised learning variational autoencoder Neurodegenerative disease Parkinson's disease

来源：评论

学校读者我要写书评

暂无评论

Situation-Aware Multivariate Time Series Anomaly Detection Through Active Learning and Contrast VAE-Based Models in Large Distributed Systems

引用

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2022年第9期40卷 2746-2765页

作者： Li, Zhihan Zhao, Youjian Geng, Yitong Zhao, Zhanxiang Wang, Hanzhang Chen, Wenxiao Jiang, Huai Vaidya, Amber Su, Liangfei Pei, Dan Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China eBay Inc San Jose CA 95125 USA

The massive amounts of monitoring data in network applications bring an urgent need for intelligent operation in large distributed systems. The key problem is precisely detecting anomalies in multivariate time series (MTS) monitoring metrics with the awareness of different application scenarios. Unsupervised NITS anomaly detection methods aim at detecting data anomalies from historical MTS without considering the out-of-band information (including user feedback and background information like code deployment status), which leads to poor performance in practice. To take advantage of the out-of-band information, we propose ACVAE, an MTS anomaly detection algorithm through active learning and contrast VAE-based detection models, which simultaneously learns NITS data's normal and anomalous patterns for anomaly detection. We also use a learnable prior to capture system status from the background information. Moreover, we propose a query model for VAE-based methods, which can learn to query labels of the must useful instances to train the detection model. We evaluate our algorithm on three different monitoring situations in eBay's search back-end systems. ACVAE achieves a range F1 score of 0.68 similar to 0.96 with only 3% labels, significantly outperforming the best competing methods by 0.18 similar to 0.50, and even better than a supervised ensemble method designed by domain experts in eBay.

关键词： Anomaly detection out-of-band information multivariate time series active learning variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Synthetic PPG Signal Generation to Improve Coronary Artery Disease Classification: Study With Physical Model of Cardiovascular System

引用

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第5期26卷 2136-2146页

作者： Mazumder, Oishee Banerjee, Rohan Roy, Dibyendu Bhattacharya, Sakyajit Ghose, Avik Sinha, Aniruddha Tata Consultancy Serv Ltd TCS Res Kolkata 700156 W Bengal India

This paper presents a novel approach of generating synthetic Photoplethysmogram (PPG) data using a physical model of the cardiovascular system to improve classifier performance with a combination of synthetic and real data. The physical model is an in-silico cardiac computational model, consisting of a four-chambered heart with electrophysiology, hemodynamic, and blood pressure auto-regulation functionality. Starting with a small number of measured PPG data, the cardiac model is used to synthesize healthy as well as PPG time-series pertaining to coronary artery disease (CAD) by varying pathophysiological parameters. A variational autoencoder (VAE) structure is proposed to derive a statistical feature space for CAD classification. Results are presented in two perspectives namely, (i) using artificially reduced real disease data and (ii) using all the real disease data. In both cases, by augmenting with the synthetic data for training, the performance (sensitivity, specificity) of the classifier changes from (i) (0.65, 1) to (1, 0.9) and (ii) (1, 0.95) to (1, 1). The proposed hybrid approach of combining physical modelling and statistical feature space selection generates realistic PPG data with pathophysiological interpretation and can outperform a baseline Generative Adversarial Network (GAN) architecture with a relatively small amount of real data for training. This proposed method could aid as a substitution technique for handling the problem of bulk data required for training machine learning algorithms for cardiac health-care applications.

关键词： Solid modeling Electrocardiography Diseases Data models Training Hemodynamics Bioinformatics Cardiac computational model coronary artery disease photoplethysmogram synthetic data variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Echocardiography Segmentation With Enforced Temporal Consistency

引用

IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第10期41卷 2867-2878页

作者： Painchaud, Nathan Duchateau, Nicolas Bernard, Olivier Jodoin, Pierre-Marc Univ Sherbrooke Dept Comp Sci Sherbrooke PQ J1K 2R1 Canada Univ Claude Bernard Lyon 1 Univ Lyon INSA Lyon UJM St EtienneCNRSInsermCREATIS UMR 5220U1294 F-69621 Lyon France Inst Univ France IUF F-75005 Paris France

Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, CNNs still struggle to leverage temporal information to provide accurate and temporally consistent segmentation maps across the whole cycle. Such consistency is required to accurately describe the cardiac function, a necessary step in diagnosing many cardiovascular diseases. In this paper, we propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints. Our method is a post-processing that takes as input segmented echocardiographic sequences produced by any state-of-the-art method and processes it in two steps to (i) identify spatio-temporal inconsistencies according to the overall dynamics of the cardiac sequence and (ii) correct the inconsistencies. The identification and correction of cardiac inconsistencies relies on a constrained autoencoder trained to learn a physiologically interpretable embedding of cardiac shapes, where we can both detect and fix anomalies. We tested our framework on 98 full-cycle sequences from the CAMUS dataset, which are available alongside this paper. Our temporal regularization method not only improves the accuracy of the segmentation across the whole sequences, but also enforces temporal and anatomical consistency.

关键词： Image segmentation Shape Echocardiography Annotations Encoding Magnetic resonance imaging Cardiac function Deep learning CNN variational autoencoder cardiac segmentation ultrasound left ventricle myocardium

来源：评论

学校读者我要写书评

暂无评论

Non-episodic variational Weight Imprinting for Few-Shot Learning

引用

SN Computer Science 2023年第3期4卷 303页

作者： Padma, V. Bhagvati, Chakravarthy School of Computer and Information Sciences University of Hyderabad Hyderabad India

Few-shot learning methods enable models to recognize novel classes from one or only a few examples. Most of the existing methods use episodic training which require multiple training models and/or variable memory requirements. We propose a non-episodic model, which combines a β-variational auto encoder (β-VAE) with the cosine similarity classifier (CSC). It is trained only once with fixed memory requirement and can be used for all test settings. We have also created our own printed character dataset with style attributes that includes six Indic scripts to analyze the effect of β-VAE’s disentangled content on few-shot classification accuracy. In our model, the encoder of β-VAE generates both the content and style distribution where only the content is used by the CSC. The network is trained in an end-to-end fashion which enables the encoder to disentangle the content features needed for classification from style attributes. After the training phase, the weights of novel classes are generated by normalizing the encoder’s content representation and used for testing. In case of Omniglot, we got 98.91% classification accuracy for 5-way 1-shot which is on par/better than the state-of-the art episodic methods. Our ablation study shows that β-VAE with CSC network outperforms CSC network on Indic script dataset. Our proposed model which is trained only once with fixed memory requirement gives comparable results with episodic and non-episodic methods as the encoder feeds the disentangled content features to the classifier. In a few settings, it also outperforms the episodic methods. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cosine similarity classifier Few-shot learning One shot learning variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Efficient and Accurate Object Extraction from Scanned Maps by Leveraging External Data and Learning Representative Context 23

Efficient and Accurate Object Extraction from Scanned Maps b...

引用

31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Duan, Weiwei Univ Southern Calif Los Angeles CA 90089 USA

ISBN: (纸本)9798400701689

This paper introduces innovative approaches for extracting geographic objects from scanned map images. Overcoming the challenges associated with labor-intensive data labeling and inaccurate extraction results, we present two data labeling methods and an accurate-enhancing extraction method. The experiment in this paper shows that our approaches outperform the baselines.

关键词： Generative models variational autoencoder Transformer Object Detection Map Processing Geospatial Information Sciences

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：