In this paper we provide a fast, data-driven solution to the failing query problem: given a query that returns an empty answer, how can one relax the query's constraints so t...
In this work, we study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to ...
This paper studies an interesting yet less explored behavior of memory access instructions, called access region locality. Unlike the traditional temporal and spatial data localit...
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...
After retrieval, this simple strategy yield to an important improvement of the average precision: from 17.02 up to 35.80. Task II. Our approach is based on argumentative structurin...