Better Exploration for Symbolic Supervision Despite the tremendous success that deep learning has shown in the past decade, there are certain application domains for which deep learning has traditionally been considered as not suitable. In particular, deep learning is notoriously known to be very data inefficient and to provide no guarantees with respect to the behaviour of the trained system. As a result of these limitations, the usage of neural networks in small data and safety critical domains has so far remained rather restricted.

Revolutionizing Archival Document Processing with AI: Enhancing Degraded Historical Document Images In recent years, the rapid advancements in Natural Language Processing (NLP) and the development of Large Language Models (LLMs) have opened new avenues for automating complex tasks across various industries. Archives, traditionally known for labor-intensive processes, are among the fields set to benefit significantly from these technologies. Historically, managing and interpreting archival documents has required manual sorting, reading, and interpreting—often under the added challenge of working with degraded or damaged materials.

Sets of points with the property that no two elements of the set are one unit distance apart are called unit-distance avoiding sets. If a point is in the unit-distance avoiding set, then the unit circle drawn around it does not intersect the set, but there is no restriction regarding the interior and the exterior of this circle. When searching for unit-distance avoiding sets with high densities, the following construction naturally comes to mind: an open disc with a unit diameter is unit-distance avoiding, as all distances between its two points are less than 1.