Sentence examples for understandable representations from inspiring English sources

It is now becoming apparent that algorithms can be designed which extract understandable representations from trained neural networks, enabling them to be used for data mining, i.e. the discovery and explanation of previously unknown relationships present in data.

This paper addresses the issues of supporting the data publishers to maintain up-to-date and machine understandable representations, separation of views (static or dynamic data) and delivering up-to-date information to data consumers in real time, helping data consumers to keep track of changes triggered from diverse environments and keeping track of evolution of the smart environment.

We envisage a situation where aresearcher, prior to embarking on a calculation, enters key parameters in a searchtool, perhaps resembling the Google interface, and that this would return renderedand understandable representations of the output of previous calculations along withlinks to the published literature.

Both older adults and occupational therapists perceived the use of CIDA as a promising solution which they believed generated intuitive and understandable representations of the home environment.

To enable cross-study analyses and efficiently finding relevant trials, the Human Studies Database (HSDB) project aims to share fully machine understandable representations of study design information between institutions [ 55].

Table  3 provides a summary of the themes, sub-themes and the associated study outcomes.> Based on the findings of this study, in terms of PU, patients see the use of CIDA as a promising solution which they believed generated intuitive and understandable representations of the home environment.

OAI-ORE/RDF: machine understandable representation of the data.

To solve this problem, researchers are interested in developing a humanly understandable representation for neural networks.

It then becomes easier to build an understandable representation of the system as an "artificial world" and to perform simulations in order to collectively test and discuss various scenarios about the resource management.

The architecture of the WWMM is built on a number of web standards and protocols, described in detail below: SWORD deposit: publish data to server Atom archive feeds: syndicate published data HTTP content negotiation: retrieve data in human and machine understandable formats OAI-ORE/RDF: machine understandable representation of the data.

As a consequence, tools are needed that collect, aggregate and interpret information from the entire event area (and possibly beyond it) and automatically generate an easily understandable representation of the relevant global crowd state parameters and their evolution in time and space (see Fig. 1).