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Active learning machine learns to create new quantum experiments [PDF]
Proceedings of the National Academy of Sciences, 2018 Significance Quantum experiments push the envelope of our understanding of fundamental concepts in quantum physics. Modern experiments have exhaustively probed the basic notions of quantum theory. Arguably, further breakthroughs require the tackling of complex quantum phenomena and consequently require complex experiments and involved ...
Alexey Melnikov +6 more
semanticscholar +8 more sources
Machine learning active-nematic hydrodynamics [PDF]
Proceedings of the National Academy of Sciences, 2020 Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such hydrodynamic parameters are difficult to derive from microscopics.
Adkins, Raymond +12 more
core +8 more sources
Calibration of uncertainty in the active learning of machine learning force fields [PDF]
Machine Learning: Science and Technology, 2023 FFLUX is a machine learning force field that uses the maximum expected prediction error (MEPE) active learning algorithm to improve the efficiency of model training.
Adam Thomas-Mitchell +2 more
doaj +3 more sources
Machine Learning for Active Portfolio Management [PDF]
The Journal of Financial Data Science, 2021 Machine learning (ML) methods are attracting considerable attention among academics in the field of finance. However, it is commonly believed that ML has not transformed the asset management industry to the same extent as other sectors. This survey focuses on the ML methods and empirical results available in the literature that matter most for active ...
Söhnke M. Bartram +3 more
openaire +3 more sources
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
doaj +2 more sources
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide [PDF]
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).
Ganesh Sivaraman +7 more
openalex +2 more sources
Active Learning Approaches to Enhancing Neural Machine Translation [PDF]
Findings, 2020 Active learning is an efficient approach for mitigating data dependency when training neural machine translation (NMT) models. In this paper, we explore new training frameworks by incorporating active learning into various techniques such as transfer ...
Yuekai Zhao +3 more
openalex +2 more sources
Integrating Iterative Machine Teaching and Active Learning into the Machine Learning Loop
Procedia Computer Science, 2021 [Abstract] Scholars and practitioners are defining new types of interactions between humans and machine learning algorithms that we can group under the umbrella term of Human-in-the-Loop Machine Learning (HITL-ML). This paper is focused on implementing two approaches to this topic—Iterative Machine Teaching (iMT) and Active Learning (AL)—and analyzing ...
Eduardo Mosqueira-Rey +2 more
openalex +4 more sources
Machine learning of molecular properties: Locality and active learning [PDF]
The Journal of Chemical Physics, 2018 In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-
Konstantin Gubaev +2 more
openaire +6 more sources
Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction [PDF]
Molecular Informatics, 2021 Computer aided synthesis planning, suggesting synthetic routes for molecules of interest, is a rapidly growing field. The machine learning methods used are often dependent on access to large datasets for training, but finite experimental budgets limit ...
Simon Johansson +6 more
openalex +2 more sources