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Robustness to noise informs on the efficiency of a representation for a given task: More efficient representations will typically tolerate more noise for a given level of performance (here, 75% accuracy).
Extremely efficient representations of the cartoon image u and texture image v exist.
Path planning in these environments must be incremental to accommodate new information and must use efficient representations.
Apart from enriching the given short texts, work has been done to extract efficient representations of those texts so as to perform information retrieval or classification.
In the present work, we have recast the same series expressions into much more computationally efficient representations using discrete Fourier transforms (DFTs).
We extended this model to efficient representations in arbitrary linear generative models by implementing lateral interactions on top of this feed-forward model.
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Efficient representation of images usually leads to improvements in storage efficiency, computational complexity and performance of image processing algorithms.
However, latitude for interpretation can also be created by syllabaries when semantic units are broken into smaller parts for more efficient representation.
The KLT can serve for this efficient representation.
Efficient representation of images can be achieved by transforms.
Thus, a high classification accuracy and an efficient representation of the ECG signals can be achieved.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com