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Interactive dialogue between user-machine learning model via explanations
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Interactive dialogue between user-machine learning model via explanations
LioNets technique applied to the Turbofan Engine Degradation Simulation dataset (time-series data) LioNets on Time Series
SHAP-Tree-MCDA is an explanation module for hierarchical Multi-Criteria Decision Aid (MCDA) models.
Set of libraries and ROS nodes to detect and track people using 2D lidar
A solver the HEX language which combines Logic Programming with Python-based plugins.
Dockerized AI4EU Acumos component for uncertainty estimation for classification networks
Dockerized/ACUMOS Ready PDDL planners (ff, fd, popf, optic-clp)
LORE (LOcal Rule-based Explanations) is a model-agnostic explanator for tabular data