Sentence examples for multiple levels of abstraction from inspiring English sources

Deep learning generally refers to methods that map data through multiple levels of abstraction, where higher levels represent more abstract entities.

Definition of multiple levels of abstraction, which include geometrical, topological and alphanumerical data.

In the centre of the framework lies design-case representation at multiple levels of abstraction.

Moreover, these systems are highly dynamic and frequent changes are anticipated across multiple levels of abstraction.

System-level design languages (SLDLs) allow designers to represent of a system at multiple levels of abstraction.

Deep learning is to apply computational models to learn data representations with multiple levels of abstraction [1].

Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, longstanding challenges for AI.

Using multiple levels of representation and abstraction, it helps a machine to understand about data (e.g., images, sound, and text) more accurately.

It is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text.

We present the use of layered probabilistic representations for modeling human activities, and describe how we use the representation to do sensing, learning, and inference at multiple levels of temporal granularity and abstraction and from heterogeneous data sources.

The purpose of using more than one binding key is to support multiple levels of protection for different abstractions of image data.