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Found 5 papers, 0 papers with code
Machine learning refinement of in situ images acquired by low electron dose LC-TEM
The time necessary for the conversion was on the order of 10ms, and we applied the model to in situ observations using the software Gatan DigitalMicrograph (DM).
Fast improvement of TEM image with low-dose electrons by deep learning
Low-electron-dose observation is indispensable for observing various samples using a transmission electron microscope; consequently, image processing has been used to improve transmission electron microscopy (TEM) images.
Dual Convolutional Neural Network for Graph of Graphs Link Prediction
Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction from graphs is not a trivial task, and much work has been done in the field of machine learning and data mining.
Jointly learning relevant subgraph patterns and nonlinear models of their indicators
In contrast, the proposed approach is based on directly learning regression trees for graph inputs using a newly derived bound of the total sum of squares for data partitions by a given subgraph feature, and thus can learn nonlinear models through standard gradient boosting.
Sparse Learning over Infinite Subgraph Features
We present a supervised-learning algorithm from graph data (a set of graphs) for arbitrary twice-differentiable loss functions and sparse linear models over all possible subgraph features.
