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Found 15 papers, 3 papers with code
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes
Our model selection criteria allow significantly faster and high quality model selection of Gaussian process models.
Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection
In industrial manufacturing, numerous tasks of visually inspecting or detecting specific objects exist that are currently performed manually or by classical image processing methods.
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.
Interpretable Anomaly Detection in Cellular Networks by Learning Concepts in Variational Autoencoders
This paper addresses the challenges of detecting anomalies in cellular networks in an interpretable way and proposes a new approach using variational autoencoders (VAEs) that learn interpretable representations of the latent space for each Key Performance Indicator (KPI) in the dataset.
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients
Partial differential equations (PDEs) are important tools to model physical systems and including them into machine learning models is an important way of incorporating physical knowledge.
"Prompt-Gamma Neutron Activation Analysis (PGNAA)" Metal Spectral Classification using Deep Learning Method
There is a pressing market demand to minimize the test time of Prompt Gamma Neutron Activation Analysis (PGNAA) spectra measurement machine, so that it could function as an instant material analyzer, e. g. to classify waste samples instantaneously and determine the best recycling method based on the detected compositions of the testing sample.
PGNAA Spectral Classification of Metal with Density Estimations
Currently, for the copper and aluminium industries, no method for the non-destructive online analysis of heterogeneous materials are available.
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations
Data in many applications follows systems of Ordinary Differential Equations (ODEs).
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design
Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empirically.
Cultural Vocal Bursts Intensity Prediction
Gaussian Processes
On boundary conditions parametrized by analytic functions
Computer algebra can answer various questions about partial differential equations using symbolic algorithms.
Including Sparse Production Knowledge into Variational Autoencoders to Increase Anomaly Detection Reliability
Digitalization leads to data transparency for production systems that we can benefit from with data-driven analysis methods like neural networks.
Singularities of Algebraic Differential Equations
Our main tools are on the one hand the algebraic resp.
Commutative Algebra Algebraic Geometry 12H05, 13P10, 34A09, 34C05, 34M35, 35A20, 57R45, 68W30
Linearly Constrained Gaussian Processes with Boundary Conditions
One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently.
An algorithmic approach to Chevalley's Theorem on images of rational morphisms between affine varieties
The goal of this paper is to introduce a new constructive geometric proof of the affine version of Chevalley's Theorem.
Algebraic Geometry
Algorithmic Linearly Constrained Gaussian Processes
We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations.
