122 citations · 218 across the 11 of their papers we have counts for
10 papers · 1 filter
Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain
Milad Moradi, Matthias Samwald
In this article, we first give an introduction to artificial intelligence and its applications in biology and medicine in Section 1. Deep learning methods are then described in Sec…
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…
Explaining Black-box Models for Biomedical Text Classification
Milad Moradi, Matthias Samwald
In this paper, we propose a novel method named Biomedical Confident Itemsets Explanation (BioCIE), aiming at post-hoc explanation of black-box machine learning models for biomedica…
Explaining black-box text classifiers for disease-treatment information extraction
Milad Moradi, Matthias Samwald
Deep neural networks and other intricate Artificial Intelligence (AI) models have reached high levels of accuracy on many biomedical natural language processing tasks. However, the…
Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
Asan Agibetov, Matthias Samwald
Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph comple…
Post-hoc explanation of black-box classifiers using confident itemsets
Milad Moradi, Matthias Samwald
Black-box Artificial Intelligence (AI) methods, e.g. deep neural networks, have been widely utilized to build predictive models that can extract complex relationships in a dataset…