检索结果-内蒙古大学图书馆

The Journal of machine learning Research 2022年第1期23卷 4632-4660页

作者： Christoffer Löffler Christopher Mutschler Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Nuremberg Germany

Active learning prioritizes the labeling of the most informative data samples. However, the performance of active learning heuristics depends on both the structure of the underlying model architecture and the data. We propose IALE, an imitation learning scheme that imitates the selection of the best-performing expert heuristic at each stage of the learning cycle in a batch-mode pool-based setting. We use DAGGER to train a transferable policy on a dataset and later apply it to different datasets and deep classifier architectures. The policy reects on the best choices from multiple expert heuristics given the current state of the active learning process, and learns to select samples in a complementary way that unifies the expert strategies. Our experiments on well-known image datasets show that we outperform state of the art imitation learners and heuristics.

关键词： active learning deep neural networks imitation learning dataset aggregation transferable policy

来源：评论

学校读者我要写书评

暂无评论

System Architecture for Reading and Interpreting Physical Printouts of Medical Forms 22

System Architecture for Reading and Interpreting Physical Pr...

引用

22nd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2021

作者： Snegireva, Ekaterina Khazankin, Grigory R. Mikheenko, Igor Stream Data Analytics and Machine Learning Laboratory Novosibirsk State University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia Meshalkin National Medical Research Center Novosibirsk Russia

ISBN: (纸本)9781665414982

This article describes the developed architecture of the system module for processing and interpreting analog medical data. Patients often undergo examinations in various medical institutions, and since their results are often handed out to the patient in printed form, the receiving institution transfers them to its database manually. There is also a tendency to completely refuse analog media and use only digital ones. But in this case, another problem appears-either loss or conversion of the accumulated analog base into digital format. These days, automatic document management systems for medical institutions-Health information systems (HIS)-are actively developing. The software module developed in accordance with the architecture described in the article can be used by developers of various HIS to automate the work with analog data. If it is necessary, it can also be freely expanded by adding new modules for working with various analog data. In this article, we take ECG scans and medical test results as examples of such data. As a result of the work undertaken the prototype of the designed system was developed and tested. © 2021 IEEE.

关键词： Medical information systems

来源：评论

学校读者我要写书评

暂无评论

A.I. and data-Driven Mobility at Volkswagen Financial Services AG

arXiv

引用

arXiv 2022年

作者： Jawed, Shayan Arif, Mofassir Ul Islam Rashed, Ahmed Madhusudhanan, Kiran Elsayed, Shereen Jameel, Mohsan Schmidt-Thieme, Lars Volk, Alexei Hintsches, Andre Kornfeld, Marlies Lange, Katrin Information Systems and Machine Learning Lab University of Hildesheim Germany Data Analytics & A.I Unit Volkswagen Financial Services AG Germany

machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research. Volkswagen Financial Services (VWFS), as a market leader in vehicle leasing services, aims to leverage existing proprietary data and the latest research to enhance existing and derive new business processes. The collaboration between Information Systems and machine learning Lab (ISMLL) and VWFS serves to realize this goal. In this paper, we propose methods in the fields of recommender systems, object detection, and forecasting that enable data-driven decisions for the vehicle life-cycle at VWFS. © 2022, CC BY-NC-ND.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Integration of Artificial Intelligence in the Organizational Adoption – A Configurational Perspective 42

Integration of Artificial Intelligence in the Organizational...

引用

42nd International Conference on Information Systems: Building Sustainability and Resilience with IS: A Call for Action, ICIS 2021 TREOs

作者： Laut, Philipp Dumbach, Philipp Eskofier, Bjoern M. University of Bamberg Kärntenstraße 7 Bamberg96047 Germany Machine Learning and Data Analytics Lab Carl-Thiersch-Straße 2b Erlangen91052 Germany

ISBN: (纸本)9781713893608

The adoption of artificial intelligence (AI) within organizations is experiencing growing interest. Since AI has distinctive characteristics (automation – augmentation), it is unclear how both characteristics influence the adoption. Besides, current research calls for a configurational perspective within the adoption theory. Building on these research gaps, we use the technology-organization-environment framework (TOE) and combine the AI characteristics ‘automation – augmentation’ and ‘product or service – process’ with the organizational and environmental characteristics: differentiation strategy, resources, entrepreneurial orientation, and network support. We collected a sample of 104 questionnairesfrom top-levelandmiddle-levelmanagersaswellas IT experts. Using fuzzy set Qualitative Comparative Analysis (fsQCA), we reveal four different solutions, namely internal integrator, supportive provider, powerful innovator, and rising enthusiast, that explain the adoption of AI. Moreover, we show that organizations adopting AI have an explicit focus on AI characteristics. Furthermore, we underline the importance of configurational thinking within the adoption. © 2021 42nd International Conference on Information Systems, ICIS 2021 TREOs: "Building Sustainability and Resilience with IS: A Call for Action". All Rights Reserved.

关键词： Automation

来源：评论

学校读者我要写书评

暂无评论

Miscellaneous EEG Preprocessing and machine learning for Pilots' Mental States Classification: Implications 22

Miscellaneous EEG Preprocessing and Machine Learning for Pil...

引用

Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Ibrahim Mohammad Alreshidi Irene Moulitsas Karl W. Jenkins Centre for Computational Engineering Sciences Cranfield University UK and Machine Learning and Data Analytics Laboratory Digital Aviation Research and Technology Centre (DARTeC) UK Centre for Computational Engineering Sciences Cranfield University UK

ISBN: (纸本)9781450396943

Higher cognitive process efforts may result in mental exhaustion, poor performance, and long-term health issues. An EEG-based methods for detecting a pilot's mental state have recently been created utilizing machine learning algorithms. EEG signals include a significant noise component, and these approaches either ignore this or use a random mix of preprocessing techniques to reduce noise. In the absence of uniform preprocessing procedures for cleaning, it would be impossible to compare the efficacy of machine learning models across research, even if they employ data obtained from the same experiment. In this study, we intend to evaluate how preprocessing approaches affect the performance of machine learning models. To do this, we concentrated on fundamental preprocessing techniques, such as a band-pass filter and independent component analysis. Using a publicly accessible actual physiological dataset gathered from a pilot who was exposed to a variety of mental events, we explore the influence of these preprocessing strategies on two machine learning models, SVMs and ANNs. Our findings indicate that the performance of the models is unaffected by preprocessing techniques. Moreover, our findings indicate that the models were able to anticipate the mental states from merged data collected in two environments. These findings demonstrate the necessity for a standardized methodological framework for the application of machine learning models to EEG inputs.

关键词： machine learning EEG preprocessing Mental states classification

来源：评论

学校读者我要写书评

暂无评论

Achieving Efficient and Realistic Full-Radar Simulations and Automatic data Annotation by exploiting Ray Meta data of a Radar Ray Tracing Simulator

arXiv

引用

arXiv 2023年

作者： Schüßler, Christian Hoffmann, Marcel Wirth, Vanessa Eskofier, Björn Weyrich, Tim Stamminger, Marc Vossiek, Martin Institute of Microwaves and Photonics Germany Chair of Visual Computing Germany Chair of Digital Reality Germany Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany

In this work a novel radar simulation concept is introduced that allows to simulate realistic radar data for Range, Doppler, and for arbitrary antenna positions in an efficient way. Further, it makes it possible to automatically annotate the simulated radar signal by allowing to decompose it into different parts. This approach allows not only almost perfect annotations possible, but also allows the annotation of exotic effects, such as multi-path effects or to label signal parts originating from different parts of an object. This is possible by adapting the computation process of a Monte Carlo shooting and bouncing rays (SBR) simulator. By considering the hits of each simulated ray, various meta data can be stored such as hit position, mesh pointer, object IDs, and many more. This collected meta data can then be utilized to predict the change of path lengths introduced by object motion to obtain Doppler information or to apply specific ray filter rules in order obtain radar signals that only fulfil specific conditions, such as multiple bounces or containing specific object IDs. Using this approach, perfect and otherwise almost impossible annotations schemes can be realized. Copyright © 2023, The Authors. All rights reserved.

关键词： Metadata

来源：评论

学校读者我要写书评

暂无评论

Carbon Stock Estimation at Scale from Aerial and Satellite Imagery

Carbon Stock Estimation at Scale from Aerial and Satellite I...

引用

Artificial Intelligence (CAI), IEEE Conference on

作者： Alex To Hoang Quoc Viet Pham Quang H. Nguyen Joseph G. Davis Barry O’Sullivan Shan L Pan Hoang D. Nguyen University of Sydney Sydney Australia University College Cork Cork Ireland Reliable Machine Learning Group Hanoi Vietnam Insight Centre for Data Analytics University College Cork Ireland Institute of Climate Risk and Response University of New South Wales Australia

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

In the ongoing efforts to mitigate climate change effect, the capability to reliably estimate forest carbon stock on a global scale is vital to support sustainable development. This entails the investigation of tree coverage from diverse forest ecosystems worldwide, necessitating a substantial volume of high-resolution images. This paper integrates a variety of remote sensing data sources, from aerial to satellite imagery, for the training and development of our AI system. Given the heterogeneous nature of these data sources, we develop a standardization method to ensure consistent image size and resolution between source platforms. Our harmonized dataset includes 86,088 training images and 21,768 validation images, each with a high resolution of 1.194 m 2 per pixel. We introduce a novel technique for tree semantic segmentation which offers a more effective alternative to traditional individual tree crown delineation for large-scale tree coverage estimation. To assess the adaptability of our AI models, we conducted experiments on a hand-annotated satellite image test set and achieved a High Vegetation IoU score of 45.73%. Building on these findings, we present an interactive web-based Geographic Information System for navigating high vegetation segmented satellite images and estimating carbon stock on a global scale.

关键词： Remote sensing Satellite images Semantic segmentation Climate change Forestry Carbon Globalization Vegetation mapping Soft sensors Image resolution Artificial intelligence Sustainable development Estimation

来源：评论

学校读者我要写书评

暂无评论

Bayesian Inference of Transition Matrices from Incomplete Graph data with a Topological Prior

arXiv

引用

arXiv 2022年

作者： Perri, Vincenzo Petrović, Luka V. Scholtes, Ingo Data Analytics Group Department of Informatics University of Zurich Switzerland Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science Julius-Maximilians-Universität Würzburg Germany

Many network analysis and graph learning techniques are based on discrete- or continuous-time models of random walks. To apply these methods, it is necessary to infer transition matrices that formalize the underlying stochastic process in an observed graph. For weighted graphs, where weighted edges capture observations of repeated interactions between nodes, it is common to estimate the entries of such transition matrices based on the (relative) weights of edges. However in real-world settings we are often confronted with incomplete data, which turns the construction of the transition matrix based on a weighted graph into an inference problem. Moreover, we often have access to additional information, which capture topological constraints of the system, i.e. which edges in a weighted graph are (theoretically) possible and which are not. Examples include transportation networks, where we may have access to a small sample of passenger trajectories as well as the physical topology of connections, or a limited set of observed social interactions with additional information on the underlying social structure. Combining these two different sources of information to reliably infer transition matrices from incomplete data on repeated interactions is an important open challenge, with severe implications for the reliability of downstream network analysis tasks. Addressing this issue, we show that including knowledge on such topological constraints can considerably improve the inference of transition matrices, especially in situations where we only have a small number of observed interactions. To this end, we derive an analytically tractable Bayesian method that uses repeated interactions and a topological prior to perform data-efficient inference of transition matrices. We compare our approach against commonly used frequentist and Bayesian approaches both in synthetic data and in five real-world datasets, and we find that our method recovers the transition probabilities with h

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

FewSOME: One-Class Few Shot Anomaly Detection with Siamese N...

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Niamh Belton Misgina Tsighe Hagos Aonghus Lawlor Kathleen M. Curran Science Foundation Ireland Centre for Research Training in Machine Learning School of Medicine University College Dublin School of Computer Science University College Dublin Insight Centre for Data Analytics University College Dublin Dublin Ireland

Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are unsuitable for settings where only a small amount of normal samples are available for training. We propose ‘Few Shot anOMaly dEtection’ (FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to accurately detect anomalies having trained on ‘few’ examples of the normal class and no examples of the anomalous class. We describe FewSOME to be of low complexity given its low data requirement and short training time. FewSOME is aided by pretrained weights with an architecture based on Siamese Networks. By means of an ablation study, we demonstrate how our proposed loss, ‘Stop Loss’, improves the robustness of FewSOME. Our experiments demonstrate that FewSOME performs at stateof-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on. Moreover, our experiments show FewSOME to be robust to contaminated datasets. We also report F1 score and balanced accuracy in addition to AUC as a benchmark for future techniques to be compared against. Code available; https://***/niamhbelton/FewSOME.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Robust Approximate Characterization of Single-Cell Heterogeneity in Microbial Growth

Robust Approximate Characterization of Single-Cell Heterogen...

引用

IEEE International Symposium on Biomedical Imaging

作者： Richard D. Paul Johannes Seiffarth Hanno Scharr Katharina Nöh IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany IBG-1: Biotechnology Forschungszentrum Jülich GmbH Jülich Germany Computational Systems Biotechnology (AVT.CSB) RWTH Aachen University Aachen Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep learning-based instance segmentation and cell tracking form the gold standard analysis tools to process the microscopy data collected, but tracking in particular suffers severely from low temporal resolution. In this work, we show that approximating cell cycle time distributions in microbial colonies of C. glutamicum is possible without performing tracking, even at low temporal resolution. To this end, we infer the parameters of a stochastic multi-stage birth process model using the Bayesian Synthetic Likelihood method at varying temporal resolutions by subsampling microscopy sequences, for which ground truth tracking is available. Our results indicate, that the proposed approach yields high quality approximations even at very low temporal resolution, where tracking fails to yield reasonable results.

关键词： Instance segmentation Image resolution Microscopy Sociology Stochastic processes Biomedical measurement Biology

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：