1 citations · 1 across the 5 of their papers we have counts for
5 papers
Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy
Vanessa Toborek, Florian Seiffarth, Sebastian Müller +1
Despite more than a decade of curriculum learning (CL) research in NLP, the field lacks a principled account of which difficulty function or scheduler to use for a given problem. T…
Invariant-Based Weight Sharing for Message Passing
Florian Seiffarth
Message-passing neural networks (MPNNs) are a powerful framework for learning representations of graph-structured domains. However, weights in MPNNs act on features only, limiting…
Rule Based Learning with Dynamic (Graph) Neural Networks
Florian Seiffarth
A common problem of classical neural network architectures is that additional information or expert knowledge cannot be naturally integrated into the learning process. To overcome…
A Fast Heuristic for Computing Geodesic Cores in Large Networks
Florian Seiffarth, Tamás Horváth, Stefan Wrobel
Motivated by the increasing interest in applications of graph geodesic convexity in machine learning and data mining, we present a heuristic for computing the geodesic convex hull…
Maximal Closed Set and Half-Space Separations in Finite Closure Systems
Florian Seiffarth, Tamas Horvath, Stefan Wrobel
Several concept learning problems can be regarded as special cases of half-space separation in abstract closure systems over finite ground sets. For the typical scenario that the c…