Results 21 to 30 of about 7,778,769 (283)
Distributional Prototypical Methods for Reliable Explanation Space Construction
IEEE Access, 2023 As deep learning has been successfully deployed in diverse applications, there is an ever increasing need to explain its decision. To explain decisions, case-based reasoning has proved to be effective in many areas.
Hyungjun Joo +3 more
doaj +1 more source
Thumbs up? Sentiment Classification using Machine Learning Techniques [PDF]
Conference on Empirical Methods in Natural Language Processing, 2002 We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform ...
B. Pang +2 more
semanticscholar +1 more source
Machine Learning for Fluid Mechanics [PDF]
Annual Review of Fluid Mechanics, 2019 The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract
S. Brunton, B. R. Noack, P. Koumoutsakos
semanticscholar +1 more source
Interpretable Machine Learning
Hands-On Machine Learning with R, 2019 Interpretable machine learning has become a popular research direction as deep neural networks (DNNs) have become more powerful and their applications more mainstream, yet DNNs remain difficult to understand. Testing with Concept Activation Vectors, TCAV,
Bradley C. Boehmke, Brandon M. Greenwell
semanticscholar +1 more source
De Computis, 2023 In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance.
Mohammad Mustafa Taye
semanticscholar +1 more source
Power Allocation Schemes Based on Deep Learning for Distributed Antenna Systems
IEEE Access, 2020 In recent years, a lot of power allocation algorithms have been proposed to maximize spectral efficiency (SE) and energy efficiency (EE) for the distributed antenna systems (DAS).
Gongbin Qian +4 more
doaj +1 more source
SoilGrids250m: Global gridded soil information based on machine learning
PLoS ONE, 2017 This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update).
T. Hengl +18 more
semanticscholar +1 more source
Small data machine learning in materials science
npj Computational Materials, 2023 This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced.
Pengcheng Xu +3 more
semanticscholar +1 more source
Italian National Conference on Sensors, 2023 Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine ...
Qi An +3 more
semanticscholar +1 more source
Quantum-chemical insights from deep tensor neural networks
Nature Communications, 2017 Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical ...
Kristof T. Schütt +4 more
doaj +1 more source