122 citations · 218 across the 11 of their papers we have counts for
4 papers · 1 filter
A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks
Kathrin Blagec, Adriano Barbosa-Silva, Simon Ott +1
Research in artificial intelligence (AI) is addressing a growing number of tasks through a rapidly growing number of models and methodologies. This makes it difficult to keep track…
Neural sentence embedding models for semantic similarity estimation in the biomedical domain
Kathrin Blagec, Hong Xu, Asan Agibetov +1
BACKGROUND: In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical…
Deep learning models are not robust against noise in clinical text
Milad Moradi, Kathrin Blagec, Matthias Samwald
Artificial Intelligence (AI) systems are attracting increasing interest in the medical domain due to their ability to learn complicated tasks that require human intelligence and ex…
Evaluating the Robustness of Neural Language Models to Input Perturbations
Milad Moradi, Matthias Samwald
High-performance neural language models have obtained state-of-the-art results on a wide range of Natural Language Processing (NLP) tasks. However, results for common benchmark dat…