Results 31 to 40 of about 8,972,433 (325)
Machine Learning for Software Engineering: Models, Methods, and Applications [PDF]
, 2018 Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data. Machine learning has been successfully applied in many areas of software engineering ranging from behaviour extraction, to testing, to bug fixing ...
Bennaceur, Amel, Meinke, Karl
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Machine learning in automated text categorization [PDF]
CSUR, 2001 The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them.
F. Sebastiani
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
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
Supervised Learning in Physical Networks: From Machine Learning to Learning Machines [PDF]
Physical Review X, 2021 18 pages, 9 ...
Menachem Stern +3 more
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Validating generic metrics of fairness in game-based resource allocation scenarios with crowdsourced annotations [PDF]
, 2011 Being able to effectively measure the notion of fairness is of vital importance as it can provide insight into the formation and evolution of complex patterns and phenomena, such as social preferences, collaboration, group structures and social conflicts.
Grappiolo, Corrado +3 more
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Physical Review D, 2022 We propose a simple method to identify a continuous Lie algebra symmetry in a dataset through regression by an artificial neural network. Our proposal takes advantage of the $ \mathcal{O}(ε^2)$ scaling of the output variable under infinitesimal symmetry transformations on the input variables. As symmetry transformations are generated post-training, the
Sean Craven +3 more
openaire +4 more sources
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2014 In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the ...
Kyunghyun Cho +6 more
semanticscholar +1 more source
Federated Learning: Collaborative Machine Learning without Centralized Training Data
International Journal of Engineering Technology and Management Sciences, 2022 Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm without transferring data samples across numerous decentralized edge devices or servers.
Abhishek V A +4 more
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