CGSchNet, a fast machine-learned model, simulates proteins with high accuracy, enabling drug discovery and protein ...

But in the idealized scenario, at initialization the network is equivalent to a Gaussian process. You can throw away your neural network and just train the kernel machine, for which you have the ...

TigerGraph, maker of a graph analytics platform for data scientists, during its Graph & AI Summit event today introduced its TigerGraph ML (Machine Learning) Workbench, a new-gen toolkit that ...

Take, for example, image recognition, which relies on a particular type of neural network known as the convolutional neural network (CNN) — so called because it uses a mathematical process known ...

Data science and machine learning features: Notebooks and Graph Neural Networks GQL still has some way to go. Standardization efforts are always complicated , and adoption is not guaranteed across ...

The process used to build most of the machine-learning models we use today can't tell if they will work in the real world or not—and that’s a problem.

Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you.