Sentence examples for complex data representations from inspiring English sources

We recently achieved several milestones in the instrument's design that improves multi-user participation when exploring complex data representations and scientific simulations.

More specifically, it aids in automatically extracting complex data representations from large volumes of unsupervised data.

Thus, the high-level complex data representations obtained by Deep Learning are useful for the application of computationally feasible and relatively simpler linear models for Big Data Analytics.

Deep Learning algorithms are one promising avenue of research into the automated extraction of complex data representations (features) at high levels of abstraction.

More specifically, since the learnt complex data representations contain semantic and relational information instead of just raw bit data, they can directly be used for semantic indexing when each data point (for example a given text document) is presented by a vector representation, allowing for a vector-based comparison which is more efficient than comparing instances based directly on raw data.

We claim that the situation is due to the inadequacy of traditional design techniques for complex and variable data representations typical for meshes.

Currently available systems do not meet the analysts' needs which are described as: visual access suitable for complex data structures, visual representations appropriate for IT-security experts, provision of workflow-specific interaction techniques, and the ability to externalize knowledge in the form of rules to ease the analysis process and to share with colleagues.

Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process.

The ability of Deep Learning to extract high-level, complex abstractions and data representations from large volumes of data, especially unsupervised data, makes it attractive as a valuable tool for Big Data Analtyics.

The model is based on a complex, data-rich representation of global supply and demand of biomass in the year 2000.

A key "secret sauce" in the success of these models is the ability of certain architectures to learn good representations of complex data; that is, preprocessing the data to facilitate optimization and generalization.