3 accepted papers
Deep generative models like variational autoencoders (VAEs) are widely used for density estimation and dimensionality reduction, but infer latent representations via amortized inference algorithms, which require that all data dimensions are observed. VAEs thus lack a key strength of probabilistic gr…
A major limiting factor in graphical model inference is the complexity of computing the partition function. Exact message-passing algorithms such as Bucket Elimination (BE) require exponential memory to compute the partition function; therefore, approximations are necessary. In this paper, we build…
Bucket Elimination (BE) is a universal inference scheme that can solve most tasks over probabilistic and deterministic graphical models exactly. However, it often requires exponentially high levels of memory (in the induced-width) preventing its execution. In the spirit of exploiting Deep Learning…