Sentence examples for input representations from inspiring English sources

Decoding distributed representations through the input representations is also possible.

DNN models can learn from input representations such as numerical encoding of texts, color pixels of images, etc., without any need to first compute application-specific descriptors45,46,47 thereby eliminating the manual step of feature engineering and representation required in conventional ML.

In the following section, we briefly describe the different input representations under study.

Several hybrid input representations were also explored to optimize both recognition accuracy and computational time.

Teacher and student networks may have different input representations, sizes, and architectures.

Besides the application of RNN, we also explore several hybrid input representations to optimize both recognition accuracy and computational time.

In each individual experiment of the ensemble, a different randomly generated noise is added to the input representation.

Additionally, a word and its context are explored to disambiguate the meaning of the word for rich input representation.

Uncertainties may originate from model structure, estimation of model parameters, data input, representation of natural variation and scaling exercises.

We demonstrate that statistical information of key movements of actions can be utilized in designing an efficient input representation, using fuzzy membership functions.

I also describe neural network models built on simple encodings of the molecular graph, moving beyond traditional fingerprint-based screening methods to a richer and more flexible input representation.