Papandreou and Yuille[4] proposed the Perturb-and-MAP Random Field, an efficient sampling method forgeneral MarkovRandom Field. We compare the detailed structure of gradients of each estimator. All gradients are formed as a summation over the steps. The Gumbel-CRF and PM-MRF estimator can be decomposed with a pathwise term, where we take gradientoff w.r.t. Since the official test set is not publically available, we use the same training/ validation/ test split as Fu et al.[1].

Most existing work in this area relies on non-probabilistic approaches or on complex Reinforcement Learning (RL)-based hard structures.

With feedbackfromthesevolunteers,wedevelopedalightweight,smallandunobtrusive prototype equipped with a tiny stereo camera and an embedded system on a single board computer to capture the samples from 10 different locations. We describe instrument development, datacollection, preprocessing, annotation, and experiments conducted.

Throughout the paper, we will use standard notations for probability unless otherwise stated, e.g.,Pr[ ]for probability,p[ ]for density,E[ ]for expectation.

P2SROisabletoparallelize PSROwith convergence guarantees bymaintaining ahierarchical pipeline ofreinforcement learning workers, each training against the policies generated by lower levels in the hierarchy.