Most current models use deterministic attention modules due to their simplicity and ease of optimization. Stochastic counterparts, on the other hand, are less popular despite their potential benefits.

We propose two novel tricks to tackle this challenge.

The performance of the proposed algorithm is evaluated on a wide variety of machine learning tasks.

While Bayesian neural networks (BNNs) hold the promise of being flexible, well-calibrated statistical models, inference often requires approximations whose consequences are poorly understood.

W e only accept 1 out of 20 papers that do not cite any other paper from our own journal.