Results 41 to 50 of about 2,307,608 (312)
Active Machine Learning for Formulation of Precision Probiotics.
International journal of pharmaceutics, 2022 It is becoming clear that the human gut microbiome is critical to health and well-being, with increasing evidence demonstrating that dysbiosis can promote disease. Increasingly, precision probiotics are being investigated as investigational drug products
Laura E. McCoubrey +6 more
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Physically regularized machine learning emulators of aerosol activation [PDF]
Geoscientific Model Development, 2020 Abstract. The activation of aerosol into cloud droplets is an important step in the formation of clouds and strongly influences the radiative budget of the Earth. Explicitly simulating aerosol activation in Earth system models is challenging due to the computational complexity required to resolve the necessary chemical and physical processes and their ...
Sam J. Silva +3 more
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Intrinsic Motivation Systems for Autonomous Mental Development [PDF]
, 2007 Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system?
Hafner, Véréna +2 more
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Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
npj Computational Materials, 2020 We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP).
G. Sivaraman +7 more
semanticscholar +1 more source
Predicting Active Antimicrobial Compounds Using Machine Learning
Trends in PharmacyBackground: Acinetobacter baumannii is a multidrug-resistant (MDR) pathogen rec ognized by the World Health Organization as a critical priority due to its high preva lence in hospital-acquired infections and limited treatment options.
İbrahim Arman
doaj +5 more sources
JMIR Medical Informatics, 2021 BackgroundThe rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy.
Wael Abdelkader +7 more
doaj +1 more source
Active-Learning Approaches for Landslide Mapping Using Support Vector Machines
Remote Sensing, 2021 Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models.
Zhihao Wang, Alexander Brenning
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Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning [PDF]
Physical review B, 2018 We propose a methodology for crystal structure prediction that is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly.
E. Podryabinkin +3 more
semanticscholar +1 more source
PAL – parallel active learning for machine-learned potentials
Digital DiscoveryAn automated, modular, and parallel active learning (PAL) library that integrates AL tasks and manages their execution and communication on shared- and distributed-memory systems using the Message Passing Interface (MPI).
Chen Zhou +7 more
openaire +5 more sources
Yoked learning in molecular data science
Artificial Intelligence in the Life SciencesActive machine learning is an established and increasingly popular experimental design technique where the machine learning model can request additional data to improve the model's predictive performance. It is generally assumed that this data is optimal
Zhixiong Li +3 more
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