Entropy-Constrained Adaptive Stochastic Quantization

Not provided in the abstract

quantizationcompressionmachine learningstochastic methods

2608.18147

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80%

1h ago

Abstract

The paper introduces a new quantization approach that optimizes Mean Squared Error while considering entropy constraints.

Reality Card

Core Claim

The formulation of the Entropy Constrained Adaptive Stochastic Quantization (ECASQ) problem allows for improved quantization that minimizes MSE under an entropy budget and unbiasedness constraint.

Method / Result

An optimal dynamic program with O(sd^2) time complexity and O(d^2) space complexity for length-d vectors and at most s quantization values.

Limitations

The existing unbiased methods do not consider the later encoding stage, which may affect the accuracy of the results.

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