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Found 5 papers, 0 papers with code
Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation
We compare our method (parOT) to related optimal transport approaches in the context of domain adaptation and domain translation on benchmark data sets.
Generative structured normalizing flow Gaussian processes applied to spectroscopic data
In particular, in the physical sciences, limited training data may not adequately characterize future observed data; it is critical that models adequately indicate uncertainty, particularly when they may be asked to extrapolate.
BB-ML: Basic Block Performance Prediction using Machine Learning Techniques
We extrapolate the basic block execution counts of GPU applications and use them for predicting the performance for large input sizes from the counts of smaller input sizes.
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra
Constructing probability densities for inference in high-dimensional spectral data is often intractable.
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses.
