Results 91 to 100 of about 9,750,100 (244)

Machine Learning Proceedings 1988 [PDF]

, 1988

openalex   +2 more sources

Climate data selection for multi-decadal wind power forecasts

Environmental Research Letters
Reliable wind speed data is crucial for applications such as estimating local (future) wind power. Global climate models (GCMs) and regional climate models (RCMs) provide forecasts over multi-decadal periods.
Sofia Morelli, Nina Effenberger, Luca Schmidt, Nicole Ludwig   +3 more
doaj   +1 more source

Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks

IEEE Access
Prediction interval (PI) is a common method to represent predictive uncertainty in regression by deep neural networks. This paper proposes an extension of the prediction interval by using a union of disjoint intervals. Since previous PI methods assumed a
Jaehak Cho, Jae Myung Kim, Seungyub Han, Jungwoo Lee   +3 more
doaj   +1 more source

Deep Learning‐Based Postprocessing to Enhance Subseasonal Soil Moisture Forecasts Over Europe

Journal of Geophysical Research: Machine Learning and Computation
Accurate forecasts on subseasonal (S2S) timescales are essential for the preparation and mitigation of the impacts of high‐impact events, such as flash droughts.
Noelia Otero, Atahan Özer, Jackie Ma
doaj   +1 more source

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

IEEE Access
The increasing demand for wind power requires more frequent inspections to identify defects in the Wind Turbine Blades (WTBs). These defects, if not detected, can compromise the structural integrity and safety of wind turbines.
Majid Memari, Praveen Shakya, Mohammad Shekaramiz, Abdennour C. Seibi, Mohammad A. S. Masoum   +4 more
doaj   +1 more source

A Machine Learning-Oriented Survey on Tiny Machine Learning

IEEE Access
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies,
Capogrosso, Luigi, Cunico, Federico, Cheng, Dong Seon, Fummi, Franco, Cristani, Marco   +4 more
openaire   +4 more sources

Filtering cells with high mitochondrial content depletes viable metabolically altered malignant cell populations in cancer single-cell studies

Genome Biology
Background Single-cell transcriptomics has transformed our understanding of cellular diversity, yet noise from technical artifacts and low-quality cells can obscure key biological signals.
Josephine Yates, Agnieszka Kraft, Valentina Boeva   +2 more
doaj   +1 more source

Machine Learning for Sociology

Annual Review of Sociology, 2019
Machine learning is a field at the intersection of statistics and computer science that uses algorithms to extract information and knowledge from data. Its applications increasingly find their way into economics, political science, and sociology. We offer a brief introduction to this vast toolbox and illustrate its current uses in the social sciences ...
Molina, Mario, Garip, Filiz
openaire   +4 more sources

Generalization of learning in a machine [PDF]

, 1959
R. J. Lee
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On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice [PDF]

Neurocomputing, 2020
Li Yang, A. Shami
semanticscholar   +1 more source