Gibbs entropy over measurement vectors is claimed to track neural-network compression performance, and a compressed-sensing pruning method (DTC) is introduced as supporting evidence.
H-theorem do-conjecture
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abstract
A pedagogical formulation of Loschmidt's paradox and H-theorem is presented with basic notation on occupancy on discrete states without invoking velocity collision operators. A conjecture, so called H-theorem do-conjecture, is formulated. Causal inference perspective on the dynamical evolution of classical many-particle system is invoked. This perspectice introduce a probabilistic view on the state of the system conditioning on the thermodyamic ensemble, i.e., function of state-variables representing the ensemble. A numerical simulation of random walkers for deterministic diffusion demonstrate the causal effect of interventional ensemble, showing a dynamical behaviour as a test of the proposed conjecture. Moreover, the chosen game like dynamics provides an accessible practical example, named Ising-Conway Entropy Game, in order to demonstrate increase in entropy over time, as a toy system of statistical physics.
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Gibbs randomness-compression proposition
Gibbs entropy over measurement vectors is claimed to track neural-network compression performance, and a compressed-sensing pruning method (DTC) is introduced as supporting evidence.