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On-Device Training of Machine Learning Models on Microcontrollers With a Look at Federated Learning

Marc Monfort Grau, Roger Pueyo Centelles, Felix Freitag
2021 Proceedings of the Conference on Information Technology for Social Good  
Recent progress in machine learning frameworks makes it now possible to run an inference with sophisticated machine learning models on tiny microcontrollers.  ...  Then, we extend the training process using federated learning among microcontrollers. Our experiments with model training show an overall trend of decreasing loss with the increase of training epochs.  ...  In recent years, the technique of federated learning has raised the interest of the research community, as it provides a means to train machine learning models on distributed devices without sharing the  ... 
doi:10.1145/3462203.3475896 fatcat:5jasw4abk5ezppdg4cwpzzahzy

The Machine Learning Algorithm for Solving the Problem of Generating Recommendations for Goods and Services
Алгоритм машинного обучения для решения задачи формирования рекомендаций товаров и услуг

V.A. Sudakov, I.A. Trofimov
2020 Modelling and Data Analysis  
The article proposes an unsupervised machine learning algorithm for assessing the most possible relationship between two elements of a set of customers and goods / services in order to build a recommendation  ...  This algorithm can be used for a "cold start" of a recommender system, when there are no labeled quality samples of training more complex models.  ...  The Machine Learning Algorithm for Solving the Problem of Generating Recommendations... Modelling and Data Analysis 2020. Vol 10, no. 4. товаров, например расстояние Жаккарда.  ... 
doi:10.17759/mda.2020100401 fatcat:u2ahppg6ljbgnmryec2okm3e6u

Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe

Caterina De Lucia, Pasquale Pazienza, Mark Bartlett
2020 Sustainability  
To fulfil the above aims, we use a combined approach of machine learning (ML) techniques and inferential (i.e., ordered logistic regression) model.  ...  Main findings suggest that ML accurately predicts ROA and ROE and indicate, through the ordered logistic regression model, the existence of a positive relationship between ESG practices and the financial  ...  Furthermore, we consider a combined approach of machine learning and inferential models to investigate ESG metrics.  ... 
doi:10.3390/su12135317 fatcat:wvhpg3yeczaurhjexrt6zs6cby

Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific - Ecuador

Diego Heras, Carlos Matovelle
2021 Revista Ambiente & Água  
Computational methods based on machine learning have had extensive development and application in hydrology, especially for modelling systems that do not have enough data.  ...  Automatic learning machines of the Python Scikit_Learn module were used; these modules integrate a wide range of automated learning algorithms, such as Linear Regression and Random Forest.  ...  Similarly, the analysis of the allocation models based on the Random Forest learning machine (see Figure 4 ) is presented.  ... 
doi:10.4136/ambi-agua.2708 fatcat:lovc43osnvfsfmu7r3cuyjhrh4

AI/ML Models to Aid in the Diagnosis of COVID-19 Illness from Forced Cough Vocalizations: Good Machine Learning Practice and Good Clinical Practices from Concept to Consumer for AI/ML Software Devices [article]

Karl Kelley, Mona Kelley, S. Caitlin Kelley, Allison A. Sakara, Maurice A. Ramirez
2021 medRxiv   pre-print
From a comprehensive and systematic search of the relevant literature on signal data signature (SDS)-based artificial intelligence/machine learning (AI/ML) systems designed to aid in the diagnosis of COVID  ...  Further comparisons were made to the recently released "Good Machine Learning Practice (GMLP) for Medical Device Development: Guiding Principles" and, in conclusions, we proposed supplemental principles  ...  server searches (n=24) • Editorials and Technical Notes (n = 7) Table 1 : Comparisons to GMLP Good Machine Learning Practice Andreu-Perez et al. 1 Verde et al.  ... 
doi:10.1101/2021.11.13.21266289 fatcat:s4xd5anolvbfxjo3peuynonyla

When Econometrics Meets Machine Learning

Eric Zheng, Yong Tan, Paulo Goes, Ramnath Chellappa, D.J. Wu, Michael Shaw, Olivia Sheng, Alok Gupta
2017 Data and Information Management  
Their function as part of the literary portrayal and narrative technique.  ...  In the concept of the aesthetic formation of knowledge and its as soon as possible and success-oriented application, insights and profits without the reference to the arguments developed around 1900.  ...  University of Singapore and Professor Han Zhang from Georgia Institute of Technology of the USA for their great support to this paper.  ... 
doi:10.1515/dim-2017-0012 fatcat:jowblljuhrgddde2pvsfkbmc5m

The Application of Tree-based model to Unbalanced German Credit Data Analysis

Zhengye Chen, Yansong Wang
2018 MATEC Web of Conferences  
We apply a series of tree-based machine learning models to analyze the German Credit Data from the UCI Repository of Machine Learning Databases.  ...  The tree-based machine learning models are particularly suitable for the unbalanced credit data by weighting the credit individuals.  ...  Machine learning [4] transforms the process of human thinking and induction into computer learning and modeling.  ... 
doi:10.1051/matecconf/201823201005 fatcat:oiuvuiusw5fyzctllybyfmqdzi

The Comparison Of Machine Learning Methods To Achieve Most Cost-Effective Prediction For Credit Card Default

Shantanu Neema, Benjamin Soibam
2017 Zenodo  
The purpose of this research is to compare seven machine learning methods to predict customer's credit card default payments in Taiwan from UCI Machine learning repository.  ...  Objective of using various machine learning methods is to predict the best possible cost-effective outcome from the risk management perspective.  ...  machine learning methods?  ... 
doi:10.5281/zenodo.851527 fatcat:aj6qqz7omze3jg3luvad344yza

Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Naeem Ali, Taher M. Ghazal, Alia Ahmed, Sagheer Abbas, M. A. Khan, Haitham M. Alzoubi, Umar Farooq, Munir Ahmad, Muhammad Adnan Khan
2022 Intelligent Automation and Soft Computing  
Fusion-based Machine learning provides a platform that may address the issues/limitations of Supply Chain Collaboration.  ...  In this scenario, the machine learningbased Supply Chain Collaboration model has been proposed to evaluate the propensity of the decision-making process to increase the efficiency of the Supply Chain Collaboration  ...  Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/iasc.2022.019892 fatcat:n3mb5uynjzhvlclwwfn6apzy6i

A review of automatic selection methods for machine learning algorithms and hyper-parameter values

Gang Luo
2016 Network Modeling Analysis in Health Informatics and Bioinformatics  
Before a machine learning model is trained, the user of a machine learning software tool typically must manually select a machine learning algorithm and set one or more model parameters termed hyper-parameters  ...  values for a given supervised machine learning problem.  ...  by the user of a machine learning software tool manually before a machine learning model is trained.  ... 
doi:10.1007/s13721-016-0125-6 dblp:journals/netmahib/Luo16 fatcat:qkxv4gp64rfvvhgw7g3xygrapm

Machine learning for neuroscience

Geoffrey E Hinton
2011 Neural Systems & Circuits  
What is machine learning? Machine learning is a type of statistics that places particular emphasis on the use of advanced computational algorithms.  ...  of a stock are examples of this type of learning); 2) in unsupervised learning, the aim is to discover good features for representing the input data; and 3) in reinforcement learning, the aim is to discover  ...  There are several good textbooks on machine learning.  ... 
doi:10.1186/2042-1001-1-12 pmid:22330889 pmcid:PMC3278390 fatcat:sltxtxtdrndm7igczotk6folhe

Potential Application of Machine Learning in Health Outcomes Research and Some Statistical Cautions

William H. Crown
2015 Value in Health  
Researchers using machine learning methods such as lasso or ridge regression should assess these models using conventional specification tests.  ...  Many machine learning methods address such limitations effectively but are still subject to the usual sources of bias that commonly arise in observational studies.  ...  The linkage of data sets should help to address many of these issues and improve the ability of machine-learning or traditional statistical methods to generate more reliable models.  ... 
doi:10.1016/j.jval.2014.12.005 pmid:25773546 fatcat:wzkwz7yxuzgc5h3blttxkm4kyq

Combining Machine Learning With Human Knowledge for Delivery Time Estimations

Markus Lochbrunner, Hans Friedrich Witschel
2022 AAAI Spring Symposia  
It was found that the pure machine learning model delivers better results than a combination of humans and machines.  ...  Thus, although the pure machine learning model delivers superior estimation accuracy than the human-machine combination, our systematic qualitative analysis of the results presents insights for future  ...  machine learning models could bring for goods transportation.  ... 
dblp:conf/aaaiss/LochbrunnerW22 fatcat:4pkioxeibzdkdif5ros7lpgtta

Review of Cyril Goutte, Nicola Cancedda, Marc Dymetman, and George Foster (eds): Learning machine translation

Philipp Koehn
2009 Machine Translation  
of machine translationa development that will be beneficial to both researchers in machine learning and machine translation.  ...  The book opens with a good general overview of current methods in statistical machine translation which touches on all major aspects.  ...  In conclusion, the book gives a good starting point to anybody interested in improving machine learning methods for problems such as machine translation, or improving machine translation with machine learning  ... 
doi:10.1007/s10590-010-9069-2 fatcat:bnwqwmbz2fekvcm3i3imarx56m

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

Hail Jung, Jinsu Jeon, Dahui Choi, Jung-Ywn Park
2021 Sustainability  
Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that  ...  The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su13084120 fatcat:fyiwe5zh2jduph2wevyo5hp24a
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