Features are input variables that are fed to machine-learning models; they are usually drawn from the columns in a dataset. Data scientists typically select and handcraft features for the model ...

While building machine learning models is fundamental to today’s narrow applications of AI, there are a variety of different ways to go about realizing the same ends.

The illustration depicts some of the innovative ideas that underpin the new machine learning model that can quickly and accurately predict the dielectric function of simple molecules, such as ...

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin introduces a breakthrough in protein simulation.

But according to Seattle-based OctoML — founded in 2019 to help enterprises optimize machine learning model deployment — there are plenty of bottlenecks — including the dependencies between ...

Machine learning algorithms. Machine learning depends on a number of algorithms for turning a data set into a model. Which algorithm works best depends on the kind of problem you’re solving, the ...

TensorFlow 2.0, released in October 2019, revamped the framework significantly based on user feedback. The result is a machine learning framework that is easier to work with—for example, by ...

The model employed a paired learning-to-rank experimental design among molecules, with participants given a straightforward cue to select their preferred compounds.