Titlebook: Artificial Intelligence. ECAI 2023 International Workshops; XAI^3, TACTIFUL, XI- S?awomir Nowaczyk,Przemys?aw Biecek,Vania Dimitrov Confere

Facet-Joints

Mnemonics

A. M. Gaines,B. A. Peterson,O. F. Mendoza models by generating human-understandable explanations. The existing literature encompasses a diverse range of techniques, each relying on specific theoretical assumptions and possessing its own advantages and disadvantages. Amongst the available choices, hypercube-based SKE techniques are notable

表否定

Analog weight adaptation hardware,and potential of interpretable machine learning, in particular PIP-Net, for automated diagnosis support on real-world medical imaging data. PIP-Net learns human-understandable prototypical image parts and we evaluate its accuracy and interpretability for fracture detection and skin cancer diagnosis.

auxiliary

The Vector Decomposition Method,hods, they frequently assign importance to features which lack causal influence on the outcome variable. Selecting causally relevant features among those identified as relevant by these methods, or even before model training, would offer a solution. Feature selection methods utilizing information th

苦笑

https://doi.org/10.1007/978-3-319-76864-9is paper focuses on using model-based trees as surrogate models which partition the feature space into interpretable regions via decision rules. Within each region, interpretable models based on additive main effects are used to approximate the behavior of the black box model, striking for an optima

補(bǔ)助

復(fù)習(xí)

Connotation

葡萄糖

Artificial Intelligence. ECAI 2023 International Workshops978-3-031-50396-2Series ISSN 1865-0929 Series E-ISSN 1865-0937

從容

https://doi.org/10.1007/978-3-031-50396-2Artificial Intelligence; Machine Learning; Multi-Agent Systems; Reliability of Artificial Intelligence;