This section provides an overview of my recent work in machine learning and data interpretation. Below, you can find summaries and links to detailed posts and papers.
DSEG-LIME: Improving Image Explanation by Hierarchical Data-Driven Segmentation
A method for enhancing image explanations through hierarchical, data-driven segmentation, aimed at improving interpretability in complex models.
Interpreting Outliers in Time Series Data through Decoding Autoencoder
This work introduces a novel approach to identifying and interpreting outliers in time series data using decoding autoencoders, advancing the field of anomaly detection.
- Status: Workshop Paper accepted at TempXAI @ ECML-PKDD 2024
- Read more: Blog Post
- Full paper on: Arxiv.org
Stay tuned for future publications and updates.