Results 11 to 20 of about 2,307,608 (312)
Machine learning of molecular properties: Locality and active learning. [PDF]
The Journal of Chemical Physics, 2017 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.
Konstantin Gubaev +2 more
semanticscholar +5 more sources
Machine learning active-nematic hydrodynamics [PDF]
Proceedings of the National Academy of Sciences, 2020 Significance Artificial intelligence holds considerable promise for transforming quantitative modeling in materials science. We illustrate this potential by developing machine-learning models of a paradigmatic class of biomaterials called active nematics.
Jonathan Colen +12 more
semanticscholar +8 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.
Söhnke M. Bartram +3 more
semanticscholar +3 more sources
Integrating Iterative Machine Teaching and Active Learning into the Machine Learning Loop
Procedia Computer Science, 2021 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).
E. Mosqueira-Rey +2 more
semanticscholar +3 more sources
Active machine learning for transmembrane helix prediction [PDF]
BMC Bioinformatics, 2010 Background About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited ...
Hatice U Osmanbeyoglu +3 more
core +5 more sources
Discovery of antimicrobial peptides in the global microbiome with machine learning.
CellNovel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87 ...
C. D. Santos-Júnior +16 more
semanticscholar +3 more sources
Universal activation function for machine learning [PDF]
Scientific Reports, 2021 AbstractThis article proposes a universal activation function (UAF) that achieves near optimal performance in quantification, classification, and reinforcement learning (RL) problems. For any given problem, the gradient descent algorithms are able to evolve the UAF to a suitable activation function by tuning the UAF’s parameters.
Brosnan Yuen +3 more
openaire +4 more sources
Machine learning forecasting of active nematics [PDF]
Soft Matter, 2021 Our model is unrolled to map an input orientation sequence (from time t-8 to t-1) to an output one (t,t + 1…) with trajectray tracing. Cyan labels are −1/2 defect while purple ones are +1/2.
Zhengyang Zhou +8 more
openaire +3 more sources
ICS: Total Freedom in Manual Text Classification Supported by Unobtrusive Machine Learning
IEEE Access, 2022 We present the Interactive Classification System (ICS), a web-based application that supports the activity of manual text classification. The application uses machine learning to continuously fit automatic classification models that are in turn used to ...
Andrea Esuli
doaj +1 more source
MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research
npj Computational Materials, 2022 Research and games both require the participant to make a series of choices. Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerate materials research.
Keith A. Brown
doaj +1 more source