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Found 4 papers, 1 papers with code
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
This paper proposes a semi-supervised methodology for training physics-informed machine learning methods.
Efficient Propagation of Uncertainty via Reordering Monte Carlo Samples
Uncertainty analysis in the outcomes of model predictions is a key element in decision-based material design to establish confidence in the models and evaluate the fidelity of models.
Deep Multimodal Transfer-Learned Regression in Data-Poor Domains
In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc.
Fast Exact Computation of Expected HyperVolume Improvement
The first grid-based algorithm has a complexity of $O(m\cdot n^m)$ with $n$ denoting the size of the nondominated set and $m$ the number of objectives.
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