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Bibliografias temáticas / Continuous and distributed machine learning

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Literatura científica selecionada sobre o tema "Continuous and distributed machine learning"

Autor: Grafiati

Publicado: 31 de agosto de 2024

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Artigos de revistas sobre o assunto "Continuous and distributed machine learning"

1

Stan, Ioan-Mihail, Siarhei Padolski, and Christopher Jon Lee. "Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3." EPJ Web of Conferences 251 (2021): 02009. http://dx.doi.org/10.1051/epjconf/202125102009.

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A large scientific computing infrastructure must offer versatility to host any kind of experiment that can lead to innovative ideas. The ATLAS experiment offers wide access possibilities to perform intelligent algorithms and analyze the massive amount of data produced in the Large Hadron Collider at CERN. The BigPanDA monitoring is a component of the PanDA (Production ANd Distributed Analysis) system, and its main role is to monitor the entire lifecycle of a job/task running in the ATLAS Distributed Computing infrastructure. Because many scientific experiments now rely upon Machine Learning al

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Yin, Zhongdong, Jingjing Tu, and Yonghai Xu. "Development of a Kernel Extreme Learning Machine Model for Capacity Selection of Distributed Generation Considering the Characteristics of Electric Vehicles." Applied Sciences 9, no. 12 (2019): 2401. http://dx.doi.org/10.3390/app9122401.

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The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, such as wind and photovoltaic (PV), and considering the charging characteristic of electric vehicles, is of great significance to the stability and economic operation of the distribution network. By using the network node voltage, the distributed generation output and the electric vehi

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Brophy, Eoin, Maarten De Vos, Geraldine Boylan, and Tomás Ward. "Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach." Sensors 21, no. 18 (2021): 6311. http://dx.doi.org/10.3390/s21186311.

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Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmo

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Vrachimis, Andreas, Stella Gkegka, and Kostas Kolomvatsos. "Resilient edge machine learning in smart city environments." Journal of Smart Cities and Society 2, no. 1 (2023): 3–24. http://dx.doi.org/10.3233/scs-230005.

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Distributed Machine Learning (DML) has emerged as a disruptive technology that enables the execution of Machine Learning (ML) and Deep Learning (DL) algorithms in proximity to data generation, facilitating predictive analytics services in Smart City environments. However, the real-time analysis of data generated by Smart City Edge Devices (EDs) poses significant challenges. Concept drift, where the statistical properties of data streams change over time, leads to degraded prediction performance. Moreover, the reliability of each computing node directly impacts the availability of DML systems,

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Musa, M. O., and E. E. Odokuma. "A framework for the detection of distributed denial of service attacks on network logs using ML and DL classifiers." Scientia Africana 22, no. 3 (2024): 153–64. http://dx.doi.org/10.4314/sa.v22i3.14.

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Despite the promise of machine learning in DDoS mitigation, it is not without its challenges. Attackers can employ adversarial techniques to evade detection by machine learning models. Moreover, machine learning models require large amounts of high-quality data for training and continuous refinement. Security teams must also be vigilant in monitoring and fine-tuning these models to adapt to new attack vectors. Nonetheless, the integration of machine learning into cybersecurity strategies represents a powerful approach to countering the persistent threat of DDoS attacks in an increasingly inter

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Oliveri, Giorgio, Lucas C. van Laake, Cesare Carissimo, Clara Miette, and Johannes T. B. Overvelde. "Continuous learning of emergent behavior in robotic matter." Proceedings of the National Academy of Sciences 118, no. 21 (2021): e2017015118. http://dx.doi.org/10.1073/pnas.2017015118.

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One of the main challenges in robotics is the development of systems that can adapt to their environment and achieve autonomous behavior. Current approaches typically aim to achieve this by increasing the complexity of the centralized controller by, e.g., direct modeling of their behavior, or implementing machine learning. In contrast, we simplify the controller using a decentralized and modular approach, with the aim of finding specific requirements needed for a robust and scalable learning strategy in robots. To achieve this, we conducted experiments and simulations on a specific robotic pla

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Kodaira, Daisuke, Kazuki Tsukazaki, Taiki Kure, and Junji Kondoh. "Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations." Energies 14, no. 21 (2021): 7340. http://dx.doi.org/10.3390/en14217340.

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Photovoltaic (PV) generation is potentially uncertain. Probabilistic PV generation forecasting methods have been proposed with prediction intervals (PIs) to evaluate the uncertainty quantitively. However, few studies have applied PIs to geographically distributed PVs in a specific area. In this study, a two-step probabilistic forecast scheme is proposed for geographically distributed PV generation forecasting. Each step of the proposed scheme adopts ensemble forecasting based on three different machine-learning methods. When individual PV generation is forecasted, the proposed scheme utilizes

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Hua, Xia, and Lei Han. "Design and Practical Application of Sports Visualization Platform Based on Tracking Algorithm." Computational Intelligence and Neuroscience 2022 (August 16, 2022): 1–9. http://dx.doi.org/10.1155/2022/4744939.

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Machine learning methods use computers to imitate human learning activities to discover new knowledge and enhance learning effects through continuous improvement. The main process is to further classify or predict unknown data by learning from existing experience and creating a learning machine. In order to improve the real-time performance and accuracy of the distributed EM algorithm for machine online learning, a clustering analysis algorithm based on distance measurement is proposed in combination with related theories. Among them, the greedy EM algorithm is a practical and important algori

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Rustam, Furqan, Muhammad Faheem Mushtaq, Ameer Hamza, Muhammad Shoaib Farooq, Anca Delia Jurcut, and Imran Ashraf. "Denial of Service Attack Classification Using Machine Learning with Multi-Features." Electronics 11, no. 22 (2022): 3817. http://dx.doi.org/10.3390/electronics11223817.

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The exploitation of internet networks through denial of services (DoS) attacks has experienced a continuous surge over the past few years. Despite the development of advanced intrusion detection and protection systems, network security remains a challenging problem and necessitates the development of efficient and effective defense mechanisms to detect these threats. This research proposes a machine learning-based framework to detect distributed DOS (DDoS)/DoS attacks. For this purpose, a large dataset containing the network traffic of the application layer is utilized. A novel multi-feature a

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Huang, Leqi. "Problems, solutions and improvements on federated learning model." Applied and Computational Engineering 22, no. 1 (2023): 183–86. http://dx.doi.org/10.54254/2755-2721/22/20231215.

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The field of machine learning has been stepping forward at a significant pace since the 21century due to the continuous modifications and improvements on the major underlying algorithms, particularly the model named federated learning (FL). This paper will specifically focus on the Partially Distributed and Coordinated Model, one of the major models subject to federated learning, to provide an analysis of the models working algorithms, existing problems and solutions, and improvements on the original model. The identification of the merits and drawbacks of each solution will be founded on docu

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Teses / dissertações sobre o assunto "Continuous and distributed machine learning"

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Armond, Kenneth C. Jr. "Distributed Support Vector Machine Learning." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/711.

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Support Vector Machines (SVMs) are used for a growing number of applications. A fundamental constraint on SVM learning is the management of the training set. This is because the order of computations goes as the square of the size of the training set. Typically, training sets of 1000 (500 positives and 500 negatives, for example) can be managed on a PC without hard-drive thrashing. Training sets of 10,000 however, simply cannot be managed with PC-based resources. For this reason most SVM implementations must contend with some kind of chunking process to train parts of the data at a time (10 ch

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Addanki, Ravichandra. "Learning generalizable device placement algorithms for distributed machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122746.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 47-50).<br>We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training. Unlike prior approaches that only find a device placement for a specific computation graph, Placeto can learn generalizable device placement policies that can be applied to any graph. We propose two key ideas in our approach: (1) we represent the

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Johansson, Samuel, and Karol Wojtulewicz. "Machine learning algorithms in a distributed context." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148920.

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Interest in distributed approaches to machine learning has increased significantly in recent years due to continuously increasing data sizes for training machine learning models. In this thesis we describe three popular machine learning algorithms: decision trees, Naive Bayes and support vector machines (SVM) and present existing ways of distributing them. We also perform experiments with decision trees distributed with bagging, boosting and hard data partitioning and evaluate them in terms of performance measures such as accuracy, F1 score and execution time. Our experiments show that the exe

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Karimi, Ahmad Maroof. "Distributed Machine Learning Based Intrusion Detection System." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470401374.

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Zam, Anton. "Evaluating Distributed Machine Learning using IoT Devices." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42388.

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Internet of things (IoT) blir bara större och större varje år och nya enheter läggs till hela tiden. Även om en stor del av dessa enheter är kontinuerligt använda finns det fortfarande väldigt många enheter som står inaktiva och sitter på oanvänd processorkraft som kan användas till att utföra maskininlärnings beräkningar. Det finns för nuvarande väldigt många metoder för att kombinera processorkraften av flera enheter för att utföra maskininlärnings uppgifter, dessa brukar kallas för distribuerade maskininlärnings metoder. huvudfokuset av detta arbetet är att utvärdera olika distribuerade mas

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Thompson, Simon Giles. "Distributed boosting algorithms." Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285529.

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Dahlberg, Leslie. "Evolutionary Computation in Continuous Optimization and Machine Learning." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35674.

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Evolutionary computation is a field which uses natural computational processes to optimize mathematical and industrial problems. Differential Evolution, Particle Swarm Optimization and Estimation of Distribution Algorithm are some of the newer emerging varieties which have attracted great interest among researchers. This work has compared these three algorithms on a set of mathematical and machine learning benchmarks and also synthesized a new algorithm from the three other ones and compared it to them. The results from the benchmark show which algorithm is best suited to handle various machin

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Ouyang, Hua. "Optimal stochastic and distributed algorithms for machine learning." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49091.

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Stochastic and data-distributed optimization algorithms have received lots of attention from the machine learning community due to the tremendous demand from the large-scale learning and the big-data related optimization. A lot of stochastic and deterministic learning algorithms are proposed recently under various application scenarios. Nevertheless, many of these algorithms are based on heuristics and their optimality in terms of the generalization error is not sufficiently justified. In this talk, I will explain the concept of an optimal learning algorithm, and show that given a time budget

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Prueller, Hans. "Distributed online machine learning for mobile care systems." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10875.

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Telecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable wi

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Konečný, Jakub. "Stochastic, distributed and federated optimization for machine learning." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/31478.

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We study optimization algorithms for the finite sum problems frequently arising in machine learning applications. First, we propose novel variants of stochastic gradient descent with a variance reduction property that enables linear convergence for strongly convex objectives. Second, we study distributed setting, in which the data describing the optimization problem does not fit into a single computing node. In this case, traditional methods are inefficient, as the communication costs inherent in distributed optimization become the bottleneck. We propose a communication-efficient framework whi

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Livros sobre o assunto "Continuous and distributed machine learning"

1

Weiss, Gerhard. Distributed machine learning. Infix, 1995.

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Testas, Abdelaziz. Distributed Machine Learning with PySpark. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9751-3.

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Amini, M. Hadi, ed. Distributed Machine Learning and Computing. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-57567-9.

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Jiang, Jiawei, Bin Cui, and Ce Zhang. Distributed Machine Learning and Gradient Optimization. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3420-8.

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Joshi, Gauri. Optimization Algorithms for Distributed Machine Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19067-4.

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Sahoo, Jyoti Prakash, Asis Kumar Tripathy, Manoranjan Mohanty, Kuan-Ching Li, and Ajit Kumar Nayak, eds. Advances in Distributed Computing and Machine Learning. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4807-6.

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Rout, Rashmi Ranjan, Soumya Kanti Ghosh, Prasanta K. Jana, Asis Kumar Tripathy, Jyoti Prakash Sahoo, and Kuan-Ching Li, eds. Advances in Distributed Computing and Machine Learning. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1018-0.

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Tripathy, Asis Kumar, Mahasweta Sarkar, Jyoti Prakash Sahoo, Kuan-Ching Li, and Suchismita Chinara, eds. Advances in Distributed Computing and Machine Learning. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-4218-3.

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Nanda, Umakanta, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Mahasweta Sarkar, and Kuan-Ching Li, eds. Advances in Distributed Computing and Machine Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1841-2.

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Chinara, Suchismita, Asis Kumar Tripathy, Kuan-Ching Li, Jyoti Prakash Sahoo, and Alekha Kumar Mishra, eds. Advances in Distributed Computing and Machine Learning. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1203-2.

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Capítulos de livros sobre o assunto "Continuous and distributed machine learning"

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Carter, Eric, and Matthew Hurst. "Continuous Delivery." In Agile Machine Learning. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5107-2_3.

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Yang, Qiang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu. "Distributed Machine Learning." In Federated Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01585-4_3.

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Galakatos, Alex, Andrew Crotty, and Tim Kraska. "Distributed Machine Learning." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80647-1.

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Galakatos, Alex, Andrew Crotty, and Tim Kraska. "Distributed Machine Learning." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_80647.

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Shultz, Thomas R., Scott E. Fahlman, Susan Craw, et al. "Continuous Attribute." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_172.

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Li, Jin, Ping Li, Zheli Liu, Xiaofeng Chen, and Tong Li. "Secure Distributed Learning." In Privacy-Preserving Machine Learning. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9139-3_4.

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Ducoulombier, Antoine, and Michèle Sebag. "Continuous mimetic evolution." In Machine Learning: ECML-98. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0026704.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Continuous Sequential Techniques." In Machine Learning in Medicine. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6886-4_18.

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Chen, Zhiyuan, and Bing Liu. "Continuous Knowledge Learning in Chatbots." In Lifelong Machine Learning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01581-6_8.

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Liu, Mark. "Q-Learning with Continuous States." In Machine Learning, Animated. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/b23383-15.

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Trabalhos de conferências sobre o assunto "Continuous and distributed machine learning"

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Belcastro, Loris, Fabrizio Marozzo, Aleandro Presta, and Domenico Talia. "A Spark-based Task Allocation Solution for Machine Learning in the Edge-Cloud Continuum." In 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT). IEEE, 2024. http://dx.doi.org/10.1109/dcoss-iot61029.2024.00090.

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Barros, Claudio D. T., Daniel N. R. da Silva, and Fabio A. M. Porto. "Machine Learning on Graph-Structured Data." In Anais Estendidos do Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbbd_estendido.2021.18179.

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Several real-world complex systems have graph-structured data, including social networks, biological networks, and knowledge graphs. A continuous increase in the quantity and quality of these graphs demands learning models to unlock the potential of this data and execute tasks, including node classification, graph classification, and link prediction. This tutorial presents machine learning on graphs, focusing on how representation learning - from traditional approaches (e.g., matrix factorization and random walks) to deep neural architectures - fosters carrying out those tasks. We also introdu

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Chepurnov, A., and N. Ershov. "APPLICATION OF MACHINE LEARNING METHODS FOR CROSS-CLASSIFICATION OF ALGORITHMS AND PROBLEMS OF MULTIVARIATE CONTINUOUS OPTIMIZATION." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.67.50.001.

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The paper is devoted to the development of a software system for the mutual classification of familiesof population optimization algorithms and problems of multivariate continuous optimization. One ofthe goals of this study is to develop methods for predicting the performance of the algorithms includedin the system and choosing the most effective algorithms from them for solving a user-specifiedoptimization problem. In addition, the proposed software system can be used to expand existing testsuites with new optimization problems. The work was carried out with the financial support of theRussia

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Sartzetakis, Ippokratis, Polyzois Soumplis, Panagiotis Pantazopoulos, Konstantinos V. Katsaros, Vasilis Sourlas, and Emmanouel Manos Varvarigos. "Resource Allocation for Distributed Machine Learning at the Edge-Cloud Continuum." In ICC 2022 - IEEE International Conference on Communications. IEEE, 2022. http://dx.doi.org/10.1109/icc45855.2022.9838647.

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Tiezzi, Matteo, Simone Marullo, Lapo Faggi, Enrico Meloni, Alessandro Betti, and Stefano Melacci. "Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/483.

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Devising intelligent agents able to live in an environment and learn by observing the surroundings is a longstanding goal of Artificial Intelligence. From a bare Machine Learning perspective, challenges arise when the agent is prevented from leveraging large fully-annotated dataset, but rather the interactions with supervisory signals are sparsely distributed over space and time. This paper proposes a novel neural-network-based approach to progressively and autonomously develop pixel-wise representations in a video stream. The proposed method is based on a human-like attention mechanism that a

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Gupta, Sujasha, Srivatsava Krishnan, and Vishnubaba Sundaresan. "Structural Health Monitoring of Composite Structures via Machine Learning of Mechanoluminescence." In ASME 2019 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/smasis2019-5697.

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Abstract The goal of this paper is to develop a machine learning algorithm for structural health monitoring of polymer composites with mechanoluminescent phosphors as distributed sensors. Mechanoluminescence is the phenomenon of light emission from organic/inorganic materials due to mechanical stimuli. Distributed sensors collect a large amount of data and contain structural response information that is difficult to analyze using classical or continuum models. Hence, approaches to analyze this data using machine learning or deep learning is necessary to develop models that describe initiation

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Mendoza, Alberto, Çağrı Cerrahoğlu, Alessandro Delfino, and Martin Sundin. "Signal Processing and Machine Learning for Effective Integration of Distributed Fiber Optic Sensing Data in Production Petrophysics." In 2022 SPWLA 63rd Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2022. http://dx.doi.org/10.30632/spwla-2022-0016.

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Distributed fibre optic sensing (DFOS) is progressively being considered in the mix of customary surveillance tools for oil and gas producing assets. Its applications are beyond monitoring of wells for production and reservoir optimization, including detection of well integrity risks and other well completion failures. However, while DFOS can uniquely yield time-dependent spatially distributed measurements, these are yet to be routinely used in formation evaluation and production logging workflows. The large volumes and complexity of time- and depth-dependent data produced by DFOS often requir

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Sadigov, Teymur, Cagri Cerrahoglu, James Ramsay, et al. "Real-Time Water Injection Monitoring with Distributed Fiber Optics Using Physics-Informed Machine Learning." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30982-ms.

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Abstract This paper introduces a novel technique that allows real-time injection monitoring with distributed fiber optics using physics-informed machine learning methods and presents results from Clair Ridge asset where a cloud-based, real-time application is deployed. Clair Ridge is a structural high comprising of naturally fractured Devonian to Carboniferous continental sandstones, with a significantly naturally fractured ridge area. The fractured nature of the reservoir lends itself to permanent deployment of Distributed Fiber Optic Sensing (DFOS) to enable real-time injection monitoring to

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"Session details: 1st Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC)." In UCC '21: 2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing, edited by Luiz F. Bittencourt, Ian Foster, and Filip De Turck. ACM, 2021. http://dx.doi.org/10.1145/3517186.

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Gao, Hongchang, Hanzi Xu, and Slobodan Vucetic. "Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/482.

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Continuous DR-submodular maximization is an important machine learning problem, which covers numerous popular applications. With the emergence of large-scale distributed data, developing efficient algorithms for the continuous DR-submodular maximization, such as the decentralized Frank-Wolfe method, became an important challenge. However, existing decentralized Frank-Wolfe methods for this kind of problem have the sample complexity of $\mathcal{O}(1/\epsilon^3)$, incurring a large computational overhead. In this paper, we propose two novel sample efficient decentralized Frank-Wolfe methods to

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Relatórios de organizações sobre o assunto "Continuous and distributed machine learning"

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Shead, Timothy, Jonathan Berry, Cynthia Phillips, and Jared Saia. Information-Theoretically Secure Distributed Machine Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1763277.

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Lee, Ying-Ying, and Kyle Colangelo. Double debiased machine learning nonparametric inference with continuous treatments. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.5419.

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Lee, Ying-Ying, and Kyle Colangelo. Double debiased machine learning nonparametric inference with continuous treatments. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.7219.

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Huang, Amy, Katelyn Barnes, Joseph Bearer, Evan Chrisinger, and Christopher Stone. Integrating Distributed-Memory Machine Learning into Large-Scale HPC Simulations. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1460078.

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Varastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.

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Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.

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Liu, Xiaopei, Dan Liu, and Cong’e Tan. Gut microbiome-based machine learning for diagnostic prediction of liver fibrosis and cirrhosis: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.5.0133.

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Review question / Objective: The invasive liver biopsy is the gold standard for the diagnosis of liver cirrhosis. Other non-invasive diagnostic approaches, have been used as alternatives to liver biopsy, however, these methods cannot identify the pathological grade of the lesion. Recently, studies have shown that gut microbiome-based machine learning can be used as a non-invasive diagnostic approach for liver cirrhosis or fibrosis, while it lacks evidence-based support. Therefore, we performed this systematic review and meta-analysis to evaluate its predictive diagnostic value in liver cirrhos

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Choquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011568.

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DATE: Wednesday, May 1, 2019 TIME: 2:00 - 3:30 p.m. ET PRESENTER: Gary Choquette, PRCI CLICK DOWNLOAD/BUY TO ACCESS THE REGISTRATION LINK FOR THIS WEBINAR Systems that manage large sets of data are becoming more common in the energy transportation industry. Having access to the data offers the opportunity to learn from previous experiences to help efficiently manage the future. But how does one manage to digest copious quantities of data to find nuggets within the ore? This webinar will outline some of the data management best practices learned from the research projects associated with CEPM.

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Visser, R., H. Kao, R. M. H. Dokht, A. B. Mahani, and S. Venables. A comprehensive earthquake catalogue for northeastern British Columbia: the northern Montney trend from 2017 to 2020 and the Kiskatinaw Seismic Monitoring and Mitigation Area from 2019 to 2020. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329078.

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To increase our understanding of induced seismicity, we develop and implement methods to enhance seismic monitoring capabilities in northeastern British Columbia (NE BC). We deploy two different machine learning models to identify earthquake phases using waveform data from regional seismic stations and utilize an earthquake database management system to streamline the construction and maintenance of an up-to-date earthquake catalogue. The completion of this study allows for a comprehensive catalogue in NE BC from 2014 to 2020 by building upon our previous 2014-2016 and 2017-2018 catalogues. Th

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Harris, L. B., P. Adiban, and E. Gloaguen. The role of enigmatic deep crustal and upper mantle structures on Au and magmatic Ni-Cu-PGE-Cr mineralization in the Superior Province. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328984.

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Aeromagnetic and ground gravity data for the Canadian Superior Province, filtered to extract long wavelength components and converted to pseudo-gravity, highlight deep, N-S trending regional-scale, rectilinear faults and margins to discrete, competent mafic or felsic granulite blocks (i.e. at high angles to most regional mapped structures and sub-province boundaries) with little to no surface expression that are spatially associated with lode ('orogenic') Au and Ni-Cu-PGE-Cr occurrences. Statistical and machine learning analysis of the Red Lake-Stormy Lake region in the W Superior Province con

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