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Found 9 papers, 4 papers with code
Effector: A Python package for regional explanations
Effector implements well-established global effect methods, assesses the heterogeneity of each method and, based on that, provides regional effects.
Regionally Additive Models: Explainable-by-design models minimizing feature interactions
Consequently, RAMs fit one component per subregion of each feature instead of one component per feature.
RHALE: Robust and Heterogeneity-aware Accumulated Local Effects
RHALE quantifies the heterogeneity by considering the standard deviation of the local effects and automatically determines an optimal variable-size bin-splitting.
DALE: Differential Accumulated Local Effects for efficient and accurate global explanations
In this paper, we propose a novel ALE approximation, called Differential Accumulated Local Effects (DALE), which can be used in cases where the ML model is differentiable and an auto-differentiable framework is accessible.
Atrapos: Real-time Evaluation of Metapath Query Workloads
In this paper, we present ATRAPOS, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching.
Simplifying Impact Prediction for Scientific Articles
Moreover, most approaches rely on the existence of rich metadata for each article, a requirement that cannot be adequately fulfilled for a large number of them.
Modeling asset allocation strategies and a new portfolio performance score
The first concerns crisis detection, which is of prime interest for the public in general and for policy makers in particular because of the significant impact that crises have on the economy.
Ranking Papers by their Short-Term Scientific Impact
A detailed experimental evaluation on four real citation datasets across disciplines, shows that our approach is more effective than previous work in ranking papers based on their short-term impact.
Publishing Life Science Data as Linked Open Data: the Case Study of miRBase
This paper presents our Linked Open Data (LOD) infrastructures for genomic and experimental data related to microRNA biomolecules.
Databases
