228 citations
- Universidad Rey Juan CarlosES23 papers
- Center for Physical Sciences and TechnologyLT6 papers
- University of Maryland, College ParkUS4 papers
- Stanford UniversityUS3 papers
- Warsaw University of TechnologyPL3 papers
- Australian National UniversityAU2 papers
- Harvard UniversityUS2 papers
- Humboldt-Universität zu BerlinDE2 papers
- Institut de Sciences des Matériaux de MulhouseFR2 papers
- RMIT UniversityAU2 papers
- University of Colorado BoulderUS2 papers
- University of PisaIT2 papers
7 papers · 1 filter
Two-Step Meta-Learning for Time-Series Forecasting Ensemble
Evaldas Vaiciukynas, Paulius Danenas, Vilius Kontrimas +1
Amounts of historical data collected increase and business intelligence applicability with automatic forecasting of time series are in high demand. While no single time series mode…
Charge-Spin Interconversion in Epitaxial Pt Probed by Spin-Orbit Torques in a Magnetic Insulator
Peng Li, Lauren J. Riddiford, Chong Bi +10
We measure spin-orbit torques (SOTs) in a unique model system of all-epitaxial ferrite/Pt bilayers to gain insights into charge-spin interconversion in Pt. With negligible electron…
Spin- and Voltage-dependent emission from Intra- and Intermolecular TADF OLEDs
Nikolai Bunzmann, Benjamin Krugmann, Sebastian Weissenseel +9
Organic light emitting diodes (OLEDs) based on thermally activated delayed fluorescence (TADF) utilize molecular systems with a small energy splitting between singlet and triplet s…
Efficient implementations of echo state network cross-validation
Mantas Lukoševičius, Arnas Uselis
Background/introduction: Cross-Validation (CV) is still uncommon in time series modeling. Echo State Networks (ESNs), as a prime example of Reservoir Computing (RC) models, are kno…
Localized convolutional neural networks for geospatial wind forecasting
Arnas Uselis, Mantas Lukoševičius, Lukas Stasytis
Convolutional Neural Networks (CNN) possess many positive qualities when it comes to spatial raster data. Translation invariance enables CNNs to detect features regardless of their…
Artificial Intelligence, Chaos, Prediction and Understanding in Science
Miguel A. F. Sanjuan
Machine learning and deep learning techniques are contributing much to the advancement of science. Their powerful predictive capabilities appear in numerous disciplines, including…