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Found 5 papers, 3 papers with code
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods
The evolving landscape of explainable artificial intelligence (XAI) aims to improve the interpretability of intricate machine learning (ML) models, yet faces challenges in formalisation and empirical validation, being an inherently unsupervised process.
Pantypes: Diverse Representatives for Self-Explainable Models
Prototypical self-explainable classifiers have emerged to meet the growing demand for interpretable AI systems.
RELAX: Representation Learning Explainability
Our approach can also model the uncertainty in its explanations, which is essential to produce trustworthy explanations.
Joint Optimization of an Autoencoder for Clustering and Embedding
That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model able to jointly learn a clustering and an embedding.
Frame-based Data Factorizations
Archetypal Analysis is the method of choice to compute interpretable matrix factorizations.
