31 citations · 61 across the 22 of their papers we have counts for
4 papers · 1 filter
xRAI: Explainable Representations through AI
Christiann Bartelt, Sascha Marton, Heiner Stuckenschmidt
We present xRAI an approach for extracting symbolic representations of the mathematical function a neural network was supposed to learn from the trained network. The approach is ba…
Neural Architecture Performance Prediction Using Graph Neural Networks
Jovita Lukasik, David Friede, Heiner Stuckenschmidt +1
In computer vision research, the process of automating architecture engineering, Neural Architecture Search (NAS), has gained substantial interest. Due to the high computational co…
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
Jovita Lukasik, David Friede, Arber Zela +2
Neural architecture search (NAS) has recently been addressed from various directions, including discrete, sampling-based methods and efficient differentiable approaches. While the…
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion
Christian Meilicke, Melisachew Wudage Chekol, Manuel Fink +1
Most of todays work on knowledge graph completion is concerned with sub-symbolic approaches that focus on the concept of embedding a given graph in a low dimensional vector space.…