Pages that link to "Item:Q2896115"
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The following pages link to How to explain individual classification decisions (Q2896115):
Displaying 27 items.
- Foundations of fine-grained explainability (Q832283) (← links)
- Learning with rationales for document classification (Q1640571) (← links)
- Explicative deep learning with probabilistic formal concepts in a natural language processing task (Q1789735) (← links)
- A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C (Q2022488) (← links)
- Scrutinizing XAI using linear ground-truth data with suppressor variables (Q2163233) (← links)
- Conclusive local interpretation rules for random forests (Q2172632) (← links)
- SIRUS: stable and interpretable RUle set for classification (Q2219234) (← links)
- Explaining individual predictions when features are dependent: more accurate approximations to Shapley values (Q2238680) (← links)
- Explanation in artificial intelligence: insights from the social sciences (Q2321252) (← links)
- Sharing hash codes for multiple purposes (Q2329853) (← links)
- Explaining AI decisions using efficient methods for learning sparse Boolean formulae (Q2331079) (← links)
- Backtransformation: a new representation of data processing chains with a scalar decision function (Q2418317) (← links)
- Rationalizing predictions by adversarial information calibration (Q2680803) (← links)
- An efficient explanation of individual classifications using game theory (Q2896017) (← links)
- Learning Optimal Decision Sets and Lists with SAT (Q5026234) (← links)
- Explainable Deep Learning: A Field Guide for the Uninitiated (Q5026262) (← links)
- A Survey on the Explainability of Supervised Machine Learning (Q5145841) (← links)
- (Q5149257) (← links)
- Comments on ``Data science, big data and statistics'' (Q5970962) (← links)
- SLISEMAP: supervised dimensionality reduction through local explanations (Q6097137) (← links)
- A framework for inherently interpretable optimization models (Q6168581) (← links)
- Considerations when learning additive explanations for black-box models (Q6176233) (← links)
- A Symbolic Approach for Counterfactual Explanations (Q6486027) (← links)
- Temporal inductive path neural network for temporal knowledge graph reasoning (Q6494380) (← links)
- Model-agnostic explanations for survival prediction models (Q6618504) (← links)
- Variable importance evaluation with personalized odds ratio for machine learning model interpretability with applications to electronic health records-based mortality prediction (Q6629964) (← links)
- Constrained dynamics, stochastic numerical methods and the modeling of complex systems. Abstracts from the workshop held May 26--31, 2024 (Q6671623) (← links)
