An analytic approximation for floating-point entropy is derived that links to a new quantity, with scale-invariance proven and closed forms given for common distributions.
Data Compression With Low Distortion and Finite Blocklength
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Thermodynamic lower bounds are approximated for exact and SGD linear regression, producing energy-aware scaling laws for optimal training dataset size given a target generalization error.
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The Entropy of Floating-Point Numbers
An analytic approximation for floating-point entropy is derived that links to a new quantity, with scale-invariance proven and closed forms given for common distributions.
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The Thermodynamic Costs of Simple Linear Regression
Thermodynamic lower bounds are approximated for exact and SGD linear regression, producing energy-aware scaling laws for optimal training dataset size given a target generalization error.