Results 51 to 60 of about 2,291,078 (312)
Risks, 2018 Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it
Udo Milkau, Jürgen Bott
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Automated discovery of a robust interatomic potential for aluminum
Nature Communications, 2021 The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for
Justin S. Smith +10 more
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An Ensemble Transfer Learning Model for Detecting Stego Images
Applied Sciences, 2023 As internet traffic grows daily, so does the need to protect it. Network security protects data from unauthorized access and ensures their confidentiality and integrity.
Dina Yousif Mikhail +2 more
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A Variational Beam Model for Failure of Cellular and Truss‐Based Architected Materials
Advanced Engineering Materials, EarlyView., 2023 Herein, a versatile and efficient beam modeling framework is developed to predict the nonlinear response and failure of cellular, truss‐based, and woven architected materials. It enables the exploration of their design space and the optimization of their mechanical behavior in the nonlinear regime. A variational formulation of a beam model is presented
Konstantinos Karapiperis +3 more
wiley +1 more source
Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters [PDF]
, 2019 We present several methods towards construction of precursors, which show great promise towards early predictions, of solar flare events in this paper.
Chen, Yang +9 more
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Active learning with support vector machines [PDF]
WIREs Data Mining and Knowledge Discovery, 2014 In machine learning, active learning refers to algorithms that autonomously select the data points from which they will learn. There are many data mining applications in which large amounts of unlabeled data are readily available, but labels (e.g., human annotations or results coming from complex experiments) are costly to obtain.
Kremer, Jan +2 more
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Uncertainty-driven dynamics for active learning of interatomic potentials
Nature Computational Science, 2023 Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, the ML
M. Kulichenko +7 more
semanticscholar +1 more source
An active role for machine learning in drug development [PDF]
Nature Chemical Biology, 2011 Because of the complexity of biological systems, cutting-edge machine-learning methods will be critical for future drug development. In particular, machine-vision methods to extract detailed information from imaging assays and active-learning methods to guide experimentation will be required to overcome the dimensionality problem in drug development.
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Active Learning for Interactive Neural Machine Translation of Data Streams [PDF]
Conference on Computational Natural Language Learning, 2018 We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a ...
Álvaro Peris, F. Casacuberta
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Scientific Reports, 2021 Machine learning is widely applied in drug discovery research to predict molecular properties and aid in the identification of active compounds. Herein, we introduce a new approach that uses model-internal information from compound activity predictions ...
Raquel Rodríguez-Pérez +1 more
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