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Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator
Max B. Paulus, Chris J. Maddison, Andreas Krause
Gradient estimation in models with discrete latent variables is a challenging problem, because the simplest unbiased estimators tend to have high variance. To counteract this, mode…
Learning Branching Heuristics for Propositional Model Counting
Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu +4
Propositional model counting, or #SAT, is the problem of computing the number of satisfying assignments of a Boolean formula. Many problems from different application areas, includ…
Gradient Estimation with Stochastic Softmax Tricks
Max B. Paulus, Dami Choi, Daniel Tarlow +2
The Gumbel-Max trick is the basis of many relaxed gradient estimators. These estimators are easy to implement and low variance, but the goal of scaling them comprehensively to larg…