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Our world is ambiguous and this is reflected in the data we use to train ouralgorithms. This is particularly true when we try to model natural processeswhere collected data is affected by noisy measurements and differences inmeasurement techniques. Sometimes, the process itself is ambiguous, such as inthe case of RNA folding, where the same nucleotide sequence can fold intodifferent structures. This suggests that a predictive model should have similarprobabilistic characteristics to match the data it models. Therefore, wepropose a hierarchical latent distribution to enhance one of the mostsuccessful deep learning models, the Transformer, to accommodate ambiguitiesand data distributions. We show the benefits of our approach (1) on a synthetictask that captures the ability to learn a hidden data distribution, (2) withstate-of-the-art results in RNA folding that reveal advantages on highlyambiguous data, and (3) demonstrating its generative capabilities onproperty-based molecule design by implicitly learning the underlyingdistributions and outperforming existing work.

Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design