Stabilizing Machine Learning for Reproducible and Explainable Results: A Novel Validation Approach to Subject-Specific Insights

16 December 2024

Papers citing "Stabilizing Machine Learning for Reproducible and Explainable Results: A Novel Validation Approach to Subject-Specific Insights"

5 / 5 papers shown

Title
Assessing the Reproducibility of Machine-learning-based Biomarker Discovery in Parkinson's Disease Ali Ameli Lourdes Peña Castillo Hamid Usefi 16 3 0 06 Apr 2023
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 127 6,293 0 22 Oct 2019
Deep Reinforcement Learning that Matters Peter Henderson Riashat Islam Philip Bachman Joelle Pineau Doina Precup David Meger OffRL 121 1,961 0 19 Sep 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,002 0 22 May 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,027 0 16 Feb 2016