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Found 8 papers, 0 papers with code
Does It Make Sense to Explain a Black Box With Another Black Box?
Most methods find those explanations by iteratively perturbing the target document until it is classified differently by the black box.
Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor Selection
Among these methods, Kernel SHAP is widely used due to its model-agnostic nature and its well-founded theoretical framework.
Effects of Locality and Rule Language on Explanations for Knowledge Graph Embeddings
None of these works has considered the impact of rules with bounded atoms such as nationality(x, England) => speaks(x, English), or the impact of learning from regions of the KG, i. e., local scopes.
s-LIME: Reconciling Locality and Fidelity in Linear Explanations
The benefit of locality is one of the major premises of LIME, one of the most prominent methods to explain black-box machine learning models.
Discovering Useful Compact Sets of Sequential Rules in a Long Sequence
We are interested in understanding the underlying generation process for long sequences of symbolic events.
HiPaR: Hierarchical Pattern-aided Regression
We introduce HiPaR, a novel pattern-aided regression method for tabular data containing both categorical and numerical attributes.
REMI: Mining Intuitive Referring Expressions on Knowledge Bases
A referring expression (RE) is a description that identifies a set of instances unambiguously.
Canonicalizing Open Knowledge Bases
Open information extraction approaches have led to the creation of large knowledge bases from the Web.
Ranked #1 on
Noun Phrase Canonicalization
on Base Dataset
