25 citations · 25 across the 3 of their papers we have counts for
3 papers
cs.AI2024
Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors
Filip Cornell, Yifei Jin, Jussi Karlgren +1
The standard evaluation protocol for measuring the quality of Knowledge Graph Completion methods - the task of inferring new links to be added to a graph - typically involves a ste…
cs.CL2021★ 25 cited
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
cs.CG2020
Using topological autoencoders as a filtering function for global and local topology
Filip Cornell
Choosing a suitable filtering function for the Mapper algorithm can be difficult due to its arbitrariness and domain-specific requirements. Finding a general filtering function tha…