Search Results for author:
Found 4 papers, 1 papers with code
Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy
Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy.
An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics
Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine.
Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data
The recent growth in data volumes produced by modern electron microscopes requires rapid, scalable, and flexible approaches to image segmentation and analysis.
A Topological-Framework to Improve Analysis of Machine Learning Model Performance
As both machine learning models and the datasets on which they are evaluated have grown in size and complexity, the practice of using a few summary statistics to understand model performance has become increasingly problematic.
