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Found 8 papers, 7 papers with code
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
In this work, we adapt state-of-the-art techniques for counterfactual generation in the domain of XAI that are based on genetic algorithms to consider a series of temporal constraints at runtime.
Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction
Predictive Process Monitoring (PPM) aims at leveraging historic process execution data to predict how ongoing executions will continue up to their completion.
Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns
Such encoded log is used to train a Machine Learning classifier to learn a mapping between the temporal patterns and the outcome of a process execution.
Machine Learning for Utility Prediction in Argument-Based Computational Persuasion
and (2) How can we identify for a new user the best utility function from amongst those that we have learned?
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs).
BIG-bench Machine Learning
Explainable artificial intelligence
+2
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
We tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph.
Logic Tensor Networks for Semantic Image Interpretation
Logic Tensor Networks (LTNs) are an SRL framework which integrates neural networks with first-order fuzzy logic to allow (i) efficient learning from noisy data in the presence of logical constraints, and (ii) reasoning with logical formulas describing general properties of the data.
