Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings

Unsupervised learning allows us to leverage unlabelled data, which has become abundantly available, and to create embeddings that are usable on a variety of downstream tasks. However, the typical lack of interpretability of unsupervised representation learning has become a limiting factor with regard to recent transparent-AI regulations. In this paper, we study graph representation learning and we show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis. We also introduce additional metrics addressing the lack of formalism and metrics in the understudied area of unsupervised-representation learning interpretability. Our results are supported by an experimental study applied to both graph-level and node-level tasks and show that interpretable embeddings provide state-of-the-art performance on subsequent downstream tasks

SVD post-compression combined with shielded-block preconditioner

In this paper, we present a post- compression method based on the Singular Value Decomposition that can be used to retrieve the low- rank representation of the Method of Moments in- teraction matrix after using the shielded-block pre- conditioner. Compression ratios of more than 17 has be achieved without significant loss of accuracy and can be further increased at the expense of the precision. The time needed to compress the pre- conditioned matrix is negligible with respect to the preconditioning time

Classical scholarship: the Byzantine contribution

Discusses the effect that Byzantine work has had on our perception of ancient Greek language and literature (especially Homer and drama)

Trials of Philosophers and Theologians under the Komnenoi

Questo articolo studia i processi per eterodossia a Bisanzio tra i secoli XI e XI