Sentence examples for representations that capture from inspiring English sources

The solution we are proposing (Langenhan et al. 2013) is based on graph representations that capture the topological relationships between spaces.

In the adopted approach [18], recursive neural networks models are used to build representations that capture the structure of the sentences, obtaining in this way their sentiment based on the meaning of each word.

In this article, we describe an approach to address the issue of automatically building and using high-level symbolic representations that capture physical interactions between objects in static configurations.

The process of speaking begins with a message-level representation that captures the idea that the speaker wishes to convey.

The formulations use a general graph representation that captures dependencies between an arbitrary number of products, technologies, and transportation paths.

The model consists of an encoder component which reads sentences from the source language and creates a representation that captures the meaning of the text provided.

Gryn et al. [22] introduce the direction map as a representation that captures the spatiotemporal distribution of motion direction across regions of interest in space and time.

By the application of Convolutional Neural Networks (CNN), we developed a vector representation that captures the essential appearance properties of hologram types in only a few values extracted from the photometric hologram stack.

The statue comprising the upper structure is modeled using 3D brick finite elements constructed from a photogrammetric representation that captures important details of the surface and allows the identification of probable zones of stress concentration.

Here, we focus on finding a global representation that captures the bulk of the genetic interactions, without requiring additional information, and employ a module hierarchy to capture functional specialization of different sub-modules.

O'Connor argues that monstrosity was a central issue in the struggle between accurate reconstructions, and representations that would capture the public's imagination.