Title: Monotone deep Boltzmann machines. Abstract: Deep Boltzmann machines refer to deep multi-layered probabilistic models, governed by a pairwise energy function that describes the likelihood of all variables in the network. Due to the difficulty of inference in such systems, they have given way largely to \emph{restricted} deep Boltzmann machines (which do not permit intra-layer or skip connections). In this paper, we propose a class of model that allows for \emph{exact, efficient} mean-field inference and learning in \emph{general} deep Boltzmann machines.  To do so, we use the tools of the recently proposed monotone Deep Equilibrium (DEQ) Model, an implicit-depth deep network that always guarantees the existence and uniqueness of its fixed points.  We show that, for a class of general deep Boltzmann machine, the mean-field fixed point can be considered as the equivalent fixed point of a monotone DEQ, which gives us a recipe for deriving an efficient mean-field inference procedure with global convergence guarantees. In addition, we show that our procedure outperforms existing mean-field approximation methods while avoiding any issue of local optima. We apply this approach to simple deep convolutional Boltzmann architectures and demonstrate that it allows for tasks such as the joint completion and classification of images, all within a single deep probabilistic setting. 