Sentence examples for data representation for from inspiring English sources

A pilot data representation for replacing the format currently used at LLNL is presented.

The tool's structured outputs can be used for either econometric research or data representation for policy analysis.

We design dedicated algorithms and data representation for the two steps of s2m: first the monomers of the database are mapped on the atomic structure, second the best tiling is computed so that non-overlapping monomers cover all the polymer structure.

We have initially worked on data representation for individual OTUs.

In this work, the intention is to create a virtual environment for industrial process and data representations for operator and engineer training.

The result will be corresponding data representations for clinical and trial data, both based on the RIM.

This demonstrates that DKLLE is able to extract the most discriminative low-dimensional embedded data representations for facial expression recognition.

Bengio et al. [5] present some characteristics of what constitutes good data representations for performing discriminative tasks, and point to the open question regarding the definition of the criteria for learning good data representations in Deep Learning.

An remaining open question is what criteria is used to define "similar" when trying to extract data representations for indexing purposes (recall, data points that are semantically similar will have similar data representations in a specific distance space).

In this section, we discuss our insights on some remaining questions in Deep Learning research, especially on work needed for improving machine learning and the formulation of the high-level abstractions and data representations for Big Data.

We conclude by identifying important future areas needing innovation in Deep Learning for Big Data Analytics, including data sampling for generating useful high-level abstractions, domain (data distribution) adaption, defining criteria for extracting good data representations for discriminative and indexing tasks, semi-supervised learning, and active learning.