Search Results for author:
Found 7 papers, 4 papers with code
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations
We tested our method on real and synthetic datasets and compared it with state-of-the-art rule-based explainers such as LORE, EXPLAN and Anchor.
Explainable Predictive Maintenance
We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
DeepVATS: Deep Visual Analytics for Time Series
DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted.
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster Prototypes
In most cases, such application is based on the transformation of an unsupervised clustering task into a supervised one and providing generalised global explanations or local explanations based on cluster centroids.
KnAC: an approach for enhancing cluster analysis with background knowledge and explanations
We argue that this can be the bottleneck in the process, especially in cases where domain knowledge exists prior to clustering.
The BIRAFFE2 Experiment. Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems
The paper describes BIRAFFE2 data set, which is a result of an affective computing experiment conducted between 2019 and 2020, that aimed to develop computer models for classification and recognition of emotion.
Generating Local Search Neighborhood with Synthesized Logic Programs
Local Search meta-heuristics have been proven a viable approach to solve difficult optimization problems.
