In stochastic systems with non-diagonal noise and switching environments, mutual information includes irreducible static and dynamic interference terms that prevent simple decomposition.
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A certified adaptive quadrature framework computes guaranteed L^p, W^{1,p}, and W^{2,p} norms of deep neural networks by propagating interval enclosures on axis-aligned boxes.
Proposes a two-gradient-field model with candidate order parameters alpha_dagger and kappa_c to unify phase transitions across learning theory and non-equilibrium chemistry.
citing papers explorer
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Emergence of information interference in stochastic systems with non-diagonal noise and switching environments
In stochastic systems with non-diagonal noise and switching environments, mutual information includes irreducible static and dynamic interference terms that prevent simple decomposition.
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Certified and accurate computation of function space norms of deep neural networks
A certified adaptive quadrature framework computes guaranteed L^p, W^{1,p}, and W^{2,p} norms of deep neural networks by propagating interval enclosures on axis-aligned boxes.
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Phase Transitions in Driven Informational Systems: A Two-Field Perspective on Learning Theory and Non-Equilibrium Chemistry
Proposes a two-gradient-field model with candidate order parameters alpha_dagger and kappa_c to unify phase transitions across learning theory and non-equilibrium chemistry.