18 citations · 44 across the 10 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
cs.LG2021★ 15 cited
Explaining Bayesian Neural Networks
Kirill Bykov, Marina M. -C. Höhne, Adelaida Creosteanu +4
To advance the transparency of learning machines such as Deep Neural Networks (DNNs), the field of Explainable AI (XAI) was established to provide interpretations of DNNs' predicti…
cs.LG2021★ 1 cited
NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights
Kirill Bykov, Anna Hedström, Shinichi Nakajima +1
Many efforts have been made for revealing the decision-making process of black-box learning machines such as deep neural networks, resulting in useful local and global explanation…