  Generalization bounds which assess the difference between the true risk and
the empirical risk have been studied extensively. However, to obtain bounds,
current techniques use strict assumptions such as a uniformly bounded or a
Lipschitz loss function. To avoid these assumptions, in this paper, we propose
a new generalization bound for Bayesian deep nets by exploiting the
contractivity of the Log-Sobolev inequalities. Using these inequalities adds an
additional loss-gradient norm term to the generalization bound, which is
intuitively a surrogate of the model complexity. Empirically, we analyze the
affect of this loss-gradient norm term using different deep nets.
