On Translations between ML Models for XAI Purposes
Authors: Alexis de Colnet, Pierre Marquis
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, the succinctness of various ML models is studied. To be more precise, the existence of polynomial-time and polynomial-space translations between representation languages for classifiers is investigated. ... We provide a complete map indicating for every pair of languages C, C whether or not a polynomial-time / polynomial-space translation exists from C to C . |
| Researcher Affiliation | Academia | Alexis de Colnet1 and Pierre Marquis2,3 1 Algorithms and Complexity Group, TU Wien, Vienna, Austria 2 Univ. Artois, CNRS, Centre de Recherche en Informatique de Lens (CRIL), F-62300 Lens, France 3Institut Universitaire de France |
| Pseudocode | No | The paper describes mathematical transformations and theoretical concepts but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve the use of datasets with train/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |