Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Entropy-Based Logic Explanations of Neural Networks
Authors: Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Pietro Lió, Marco Gori, Stefano Melacci6046-6054
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider four different case studies to demonstrate that: (i) this entropy-based criterion enables the distillation of concise logic explanations in safety-critical domains from clinical data to computer vision; (ii) the proposed approach outperforms state-of-the-art white-box models in terms of classification accuracy and matches black box performances. ... 5 Experiments The quality of the explanations and the classification performance of the proposed approach are quantitatively assessed and compared to state-of-the-art white-box models. |
| Researcher Affiliation | Academia | Pietro Barbiero1, Gabriele Ciravegna 2,3,4, Francesco Giannini 3, Pietro Li o 1, Marco Gori 3,4, Stefano Melacci 3, 1 University of Cambridge (UK) 2 Universit a di Firenze (Italy) 3 Universit a di Siena (Italy) 4 Universit e Cˆote d Azur (France) |
| Pseudocode | No | No section labeled 'Pseudocode' or 'Algorithm' or clearly formatted pseudocode block was found in the provided text. |
| Open Source Code | Yes | Finally, we share an implementation of the entropy layer, with extensive documentation and all the experiments in the public repository: https://github.com/pietrobarbiero/entropy-lens. |
| Open Datasets | Yes | Will we recover from ICU? (MIMIC-II). The Multiparameter Intelligent Monitoring in Intensive Care II (MIMICII, (Saeed et al. 2011; Goldberger et al. 2000)) is a public-access intensive care unit (ICU) database... What kind of democracy are we living in? (V-Dem). Varieties of Democracy (V-Dem, (Pemstein et al. 2018; Coppedge et al. 2021)) dataset contains a collection of indicators... What does parity mean? (MNIST Even/Odd). The Modified National Institute of Standards and Technology database (MNIST, (Le Cun 1998)) contains a large collection of images... What kind of bird is that? (CUB). The Caltech-UCSD Birds-200-2011 dataset (CUB, (Wah et al. 2011)) is a fine-grained classification dataset. |
| Dataset Splits | Yes | The results for each metric are reported in terms of mean and standard error, computed over a 5-fold cross validation (Krzywinski and Altman 2013). |
| Hardware Specification | No | No specific hardware details (e.g., CPU model, GPU model, memory specifications) used for running experiments were found in the provided text. |
| Software Dependencies | No | While software names like 'PyTorch' and 'Scikit-learn' are mentioned, specific version numbers for these or other dependencies were not provided in the text to ensure reproducibility. |
| Experiment Setup | No | No specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations were found in the provided text. |