Search Results for author:
Found 2 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.
StairNetV3: Depth-aware Stair Modeling using Deep Learning
Experiments on our dataset show that our method has a significant improvement over the previous best monocular vision method, with an intersection over union (IOU) increase of 3. 4 %, and the lightweight version has a fast detection speed and can meet the requirements of most real-time applications.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
