Results 41 to 50 of about 154,891 (246)

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, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin, Kipton Barros   +10 more
doaj   +1 more source

Streaming Machine Learning and Online Active Learning for Automated Visual Inspection.

IFAC-PapersOnLine, 2022
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided ...
Rožanec, Jože M., Trajkova, Elena, Dam, Paulien, Fortuna, Blaž, Mladenić, Dunja   +4 more
openaire   +2 more sources

Predicting Active Antimicrobial Compounds Using Machine Learning

Trends in Pharmacy
Background: 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   +3 more sources

Incorporating Active Learning into Machine Learning Techniques for Sensory Evaluation of Food

International Journal of Computational Intelligence Systems, 2020
The sensory evaluation of food quality using a machine learning approach provides a means of measuring the quality of food products. Thus, this type of evaluation may assist in improving the composition of foods and encouraging the development of new ...
Nhat-Vinh Lu, Roengchai Tansuchat, Takaya Yuizono, Van-Nam Huynh   +3 more
doaj   +1 more source

Onception: Active Learning with Expert Advice for Real World Machine Translation

Computational Linguistics, 2023
Active learning can play an important role in low-resource settings (i.e., where annotated data is scarce), by selecting which instances may be more worthy to annotate.
Vânia Mendonça, Ricardo Rei, Luísa Coheur, Alberto Sardinha   +3 more
doaj   +1 more source

Towards high entropy alloy with enhanced strength and ductility using domain knowledge constrained active learning

Materials & Design, 2022
The simultaneous optimization of competing properties is a challenge of machine learning based materials design. We proposed a domain knowledge constrained active learning loop for the design of high entropy alloys with optimized strength and ductility ...
Hongchao Li, Ruihao Yuan, Hang Liang, William Yi Wang, Jinshan Li, Jun Wang   +5 more
doaj   +1 more source

Enzyme activity from machine learning [PDF]

Science, 2019
Enzyme Engineering Enzymes are very efficient catalysts for biochemical reactions, which are increasingly important for industrial applications. However, incomplete knowledge of the key factors that induce their catalytic properties limits our ability to engineer new enzymes with new properties. Bonk et al.
openaire   +1 more source

Using human brain activity to guide machine learning [PDF]

Scientific Reports, 2018
AbstractMachine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source ofinspirationfor machine learning, little effort has been made to
Fong, Ruth C., Scheirer, Walter, Cox, David   +2 more
openaire   +5 more sources

Mapping the evolution of mitochondrial complex I through structural variation

FEBS Letters, EarlyView.
Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin, Tingting Chen, James A. Letts   +2 more
wiley   +1 more source

Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes

The Journal of Physical Chemistry A, 2023
A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the low energy structures, their high computational cost is problematic because of the need to evaluate ...
Patrick W. V. Butler, Roohollah Hafizi, Graeme Day   +2 more
openaire   +4 more sources