What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic ComponentsJan 1, 2022·Kacper Sokol,Alexander Hepburn,Raúl Santos-Rodríguez,Peter A. Flach· 0 min read URL Cite DOITypeJournal articlePublicationCoRRpublicationsLast updated on Jan 1, 2022 AuthorsKacper SokolPostdoc← What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei Challenges Jan 1, 2022A Longitudinal Study of Pervasive Display Personalisation Jan 1, 2021 →
