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Found 5 papers, 1 papers with code
Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs
Biomedical datasets are often modeled as knowledge graphs (KGs) because they capture the multi-relational, heterogeneous, and dynamic natures of biomedical systems.
Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey
As knowledge graphs are becoming a popular way to represent heterogeneous and multi-relational data, methods for reasoning on graph structures have attempted to follow this neurosymbolic paradigm.
Alignment-based conformance checking over probabilistic events
However, this data is typically characterised by noise and uncertainty, in contrast to the assumption of a deterministic event log required by conformance checking algorithms.
Constrained Training of Neural Networks via Theorem Proving
We introduce a theorem proving approach to the specification and generation of temporal logical constraints for training neural networks.
Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction
The interpretability of the black box machine learning models is evaluated using two post-hoc explanation techniques, LIME and SHAP.
BIG-bench Machine Learning
Interpretable Machine Learning
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