Sentence examples for learning representations from inspiring English sources

In this work, we develop and study additional techniques for learning representations of goals.

This strategy is called sparse coding that is often used in dictionary learning representations.

For example, following learning representations and patterns from the unlabeled/unsupervised data, the available labeled/supervised data can be exploited to further tune and improve the learnt representations and patterns for a specific analytics task, including semantic indexing or discriminative modeling.

A fast learning procedure is presented that allows symmetric networks to learn representations of unknown logic formulas by looking at examples.

"For example," the CMU students write, "babies push objects, poke them, put them in their mouth and throw them to learn representations.

On the other word, Deep Learning can learn representations of the Big Data in a Deep Architecture with multiple levels of representations.

These transformations represent the data, so Deep Learning can be considered as special case of representation learning algorithms which learn representations of the data in a Deep Architecture with multiple levels of representations.

One example is image recognition, where it is often necessary to learn representations of the underlying components of images, such as objects, object-parts, or features.

The multineural network model presented in this work, allows acquisition of different neural representations of the grasping task through a successive learning over two stages in a strategy that uses already learned representations for the acquisition of the subsequent knowledge.

Compared with traditional fault diagnosis approaches, DBN-based deep learning architecture can automatically learn representations from the input and reduce the manual work so that it can reduce the influence of artificial factors.

This technology uses a biologically inspired model known as an 'artificial neural network' which is trained using vast quantities of data to learn representations of data, so that it can make predictions.