Evaluating Explanations | Biagio (Mattia) La Rosa

This page provides a summary sheet that includes the general goal, reference papers (both mine and external) for an overview of the topic, as well as the domains explored so far. We are also interested in extending the applications of these techniques beyond their traditional domains. If you have expertise in other areas (e.g., neuroscience, gaming, or audio/speech modeling), we would be happy to explore potential extensions into those fields.

Goal: The goal of this research area is to standardize evaluation procedures for explanations. This research direction aims to create novel metrics to measure properties of interest, unify the existing ones, discover pitfalls in the current evaluation procedures, and develop novel benchmarks.

Domains: Vision.

Reference Papers:

Metrics for Neuron Explanations: [(La Rosa et al., 2023)] [(Makinwa et al., 2022)]
Metrics for Memory-based Explanations: [(La Rosa et al., 2022)]

References

2023

Conference

Towards a fuller understanding of neurons with Clustered Compositional Explanations

Biagio La Rosa, Leilani H. Gilpin, and Roberto Capobianco

In Thirty-seventh Conference on Neural Information Processing Systems, 2023

Bib Official PDF Code

@inproceedings{LaRosa2023Towards,
  title = {Towards a fuller understanding of neurons with Clustered Compositional Explanations},
  author = {{La Rosa}, Biagio and Gilpin, Leilani H. and Capobianco, Roberto},
  booktitle = {Thirty-seventh Conference on Neural Information Processing Systems},
  year = {2023},
  url = {https://openreview.net/forum?id=51PLYhMFWz},
}

2022

Conference

Detection Accuracy for Evaluating Compositional Explanations of Units

Sayo M. Makinwa, Biagio La Rosa, and Roberto Capobianco

In AIxIA 2021 - Advances in Artificial Intelligence, 2022

Bib DOI Official PDF Code

@incollection{Makinwa2022,
  author = {Makinwa, Sayo M. and {La Rosa}, Biagio and Capobianco, Roberto},
  booktitle = {AIxIA 2021 - Advances in Artificial Intelligence},
  publisher = {Springer International Publishing},
  title = {Detection Accuracy for~Evaluating Compositional Explanations of~Units},
  year = {2022},
  pages = {550--563},
  doi = {10.1007/978-3-031-08421-8_38},
}

Journal

A self-interpretable module for deep image classification on small data

Biagio La Rosa, Roberto Capobianco, and Daniele Nardi

Applied Intelligence, 2022

Bib DOI Official PDF Code

@article{LaRosa2022,
  author = {La Rosa, Biagio and Capobianco, Roberto and Nardi, Daniele},
  journal = {Applied Intelligence},
  title = {A self-interpretable module for deep image classification on small data},
  year = {2022},
  issn = {1573-7497},
  number = {8},
  pages = {9115--9147},
  volume = {53},
  doi = {10.1007/s10489-022-03886-6},
  publisher = {Springer Science and Business Media LLC},
}