Online reviews play a significant role in influencing decisions made by users in day-to-day
life. The presence of reviewers who deliberately post fake reviews for financial or other …

Um Maschinelles Lernen (ML) in sicherheitskritischen oder autonomen Systemen
einzusetzen sind G̈utegarantien und Fehlerschranken erforderlich—eine rein empirische …

Statistical relational learning (SRL) frameworks are effective at defining probabilistic models
over complex relational data. They often use weighted first-order logical rules where the …

Existing techniques for improving scalability of weight learning in Markov Logic Networks
(MLNs) are typically effective when the parameters of the MLN are tied, ie, several ground …

Markov logic networks (MLNs) represent the underlying domain using a set of weighted first-
order formulas and have been successfully applied to a variety of real world problems. A …

Parameter tying is a regularization method in which parameters (weights) of a machine
learning model are partitioned into groups by leveraging prior knowledge and all …

Probabilistic soft logic (PSL) is a statistical relational learning framework that represents
complex relational models with weighted first-order logical rules. The weights of the rules in …

Abstract Templated Statistical Relational Learning languages, such as Markov Logic
Networks (MLNs) and Probabilistic Soft Logic (PSL), offer much of the expressivity of …

We propose a simple and easy to implement neural network compression algorithm that
achieves results competitive with more complicated state-of-the-art methods. The key idea is …

In our increasingly connected world, data comes from many different sources, in many
different forms, and is noisy, complex, and structured. To confront modern data, we need to …