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Found 5 papers, 1 papers with code
Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks
With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated.
CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation
Remarkably, our model reduces by 33. 2\%, and 15. 5\% the number of clicks required to surpass an IoU of 0. 95 in the previous state-of-the-art approach on the Berkeley and DAVIS sets, respectively.
Ranked #1 on
Interactive Segmentation
on GrabCut
An Efficient Instance Segmentation Approach for Extracting Fission Gas Bubbles on U-10Zr Annular Fuel
In the advanced annular U-10Zr fuel, the lanthanides present as fission gas bubbles.
Advanced Characterization-Informed Framework and Quantitative Insight to Irradiated Annular U-10Zr Metallic Fuels
Conclusive findings were obtained and allowed for evaluation of the lanthanide transportation through connected bubbles based on approximately 67, 000 fission gas bubbles of the two advanced samples.
Understanding Fission Gas Bubble Distribution, Lanthanide Transportation, and Thermal Conductivity Degradation in Neutron-irradiated α-U Using Machine Learning
UZr based metallic nuclear fuel is the leading candidate for next-generation sodium-cooled fast reactors in the United States.
