Learning CNN Filters via Generalized Stein’s Method

Posted by klaurent Peking

Kavli Affiliate: Feng Long| Summary: Convolutional Neural Networks (CNNs) have undoubtedly revolutionized image data analysis and the field of computer vision. As the cornerstone of CNNs, the convolution operation enables the networks to extract abstract features and uncover hidden relationships in the image data. This paper considers the problem of estimating convolution filters from a […]


Continue.. Learning CNN Filters via Generalized Stein’s Method

Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization

Posted by klaurent Cornell

Kavli Affiliate: David Muller | Summary:Estimating physical parameters from scientific images is a common inverse problem in materials characterization that often relies on expensive physics-based simulations. In electron microscopy, specimen thickness and crystal mistilt are critical parameters that govern how electrons scatter through the sample, and therefore the accuracy of any atomic-scale structure recovered from […]


Continue.. Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization