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These highly efficient codes are publicly available in the package ACDC.
Information theory was created to find practical ways to make better, more efficient codes and find the limits on how fast computers could process digital signals.
For many other efficient codes, we have to fit the parameters of a model (e.g., the binomial distribution) to the distribution of gaps in the index.
Besides, the development of coupling strategies and efficient codes open new prospects due to ongoing reduction of calculation cost which was again recently the major drawback of discrete approaches.
One of the main findings is that careful selection of numerical methods and implementation techniques can lead to accurate and very efficient codes, where the geometric complexity does not lead to algorithmic or numerical complexity.
Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers.
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Efficient code generation underlies much of our work.
The efficient coding hypothesis has revolutionized theoretical neuroscience.
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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