74 citations · 162 across the 8 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…
GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain
Milad Moradi, Kathrin Blagec, Florian Haberl +1
Deep neural language models have set new breakthroughs in many tasks of Natural Language Processing (NLP). Recent work has shown that deep transformer language models (pretrained o…
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…