<ul><li>Researchers have proposed a new Fast Entropy Approximation (FEA) method to approximate Shannon entropy and its gradient with a mean absolute error of $10^{-3}, significantly lower than existing methods.</li><li>FEA allows for around 50% faster computation compared to current algorithms, requiring only 5 to 6 elementary computational operations.</li><li>On machine learning benchmarks, FEA results in better model quality, faster feature extraction, and computational cost reduction by two to three orders of magnitude.</li><li>This new method addresses the singularity of the Shannon entropy gradient, leading to improved robustness and convergence in tools used across various fields like physics, information theory, machine learning, and quantum computing.</li></ul>

Fast and close Shannon entropy approximation

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