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Found 9 papers, 1 papers with code
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space.
On Extreme Value Asymptotics of Projected Sample Covariances in High Dimensions with Applications in Finance and Convolutional Networks
Maximum-type statistics of certain functions of the sample covariance matrix of high-dimensional vector time series are studied to statistically confirm or reject the null hypothesis that a data set has been collected under normal conditions.
Online Detection of Changes in Moment-Based Projections: When to Retrain Deep Learners or Update Portfolios?
Open-end as well as closed-end monitoring is studied under mild assumptions on the training sample and the observations of the monitoring period.
Cross-Validation and Uncertainty Determination for Randomized Neural Networks with Applications to Mobile Sensors
Results are discussed about supervised learning with such networks and regression methods in terms of consistency and bounds for the generalization and prediction error.
Is there a role for statistics in artificial intelligence?
The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion.
Consistency of Extreme Learning Machines and Regression under Non-Stationarity and Dependence for ML-Enhanced Moving Objects
Supervised learning by extreme learning machines resp.
Automatic Processing and Solar Cell Detection in Photovoltaic Electroluminescence Images
The next important step is the extraction of the solar cells of a PV module, for instance to pass them to a procedure to detect and analyze defects on their surface.
Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images
Automated segmentation of cells is therefore a key step in automating the visual inspection workflow.
On Convergence of Moments for Approximating Processes and Applications to Surrogate Models
This problem especially arises when working with surrogate models, e. g. to enrich observed data by simulated data, where the surrogates $Y_n$'s are constructed to justify that they approximate the $ X_n $'s.
