… approximation and thus efficient parameter learning. Moreover… to manipulate the model and
evaluate various marginal and … be possible since we evaluate the log-likelihood derivative: …

… the applicability of perturb-and-MAP to new scenarios (we … enables efficient learning: we
can efficiently approximate the … true log-likelihood, or as the true gradient of the approximate …

… PM is a two step process: (i) perturb the model using Gumbel … However, this is feasible only
if efficient MAP solvers are … a toy problem in which exact log-likelihood can be calculated, as …

… Minimizing this loss amounts to maximizing the log-likelihood of the … it is also called
perturbmax or perturb-and-map model. … In order to model posterior distributions that allow efficient …

… – We show in Section 3 how “perturb-and-MAP” ideas based on the Gumbel distribution and
efficient discretization can … Osokin, “Marginal weighted maximum log-likelihood for efficient …

… deep generative models for modeling the marginal distribution … does not correspond to the
log-likelihood of the joint distribution. … variables and apply the Perturb-and-MAP method [33]. …

… perturb and MAP technique [36] makes the relation even more precise. In this work we apply
maxmarginal … -fine scheme, we apply at each pixel i the negative log-likelihood loss ℓNLL(Bl …

… Herding may be interpreted as a" perturb and map" method where the … projections of these
moments on the marginal polytope. … limit in Equation 4.6, the log-likelihood function becomes …

… When the latent structure in the model matches the true … to marginal inference in the exponential
family, and efficient … bound (ELBO) on the marginal log-likelihood of the time series. We …

… A structural equation model (SEM) is an effective framework … of the squared loss to the
log-likelihood in equation (1), and … problem of learning an ordering for autoregressive models. …