N
Natalia Díaz-Rodríguez
Researcher at French Institute for Research in Computer Science and Automation
Publications - 61
Citations - 5984
Natalia Díaz-Rodríguez is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Computer science & Reinforcement learning. The author has an hindex of 17, co-authored 45 publications receiving 2050 citations. Previous affiliations of Natalia Díaz-Rodríguez include École Normale Supérieure & University of California, Santa Cruz.
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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: In this paper, a taxonomy of recent contributions related to explainability of different machine learning models, including those aimed at explaining Deep Learning methods, is presented, and a second dedicated taxonomy is built and examined in detail.
Posted Content
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: Previous efforts to define explainability in Machine Learning are summarized, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought, and a taxonomy of recent contributions related to the explainability of different Machine Learning models are proposed.
Journal ArticleDOI
State representation learning for control: An overview.
TL;DR: This survey aims at covering the state-of-the-art on state representation learning in the most recent years by reviewing different SRL methods that involve interaction with the environment, their implementations and their applications in robotics control tasks (simulated or real).
Posted Content
Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges
Timothée Lesort,Vincenzo Lomonaco,Andrei Stoian,Davide Maltoni,David Filliat,Natalia Díaz-Rodríguez +5 more
TL;DR: ContinContinual Learning (CL) is a particular machine learning paradigm where the data distribution and learning objective changes through time, or where all the training data and objective criteria are never available at once as mentioned in this paper.
Journal ArticleDOI
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
Timothée Lesort,Vincenzo Lomonaco,Andrei Stoian,Davide Maltoni,David Filliat,Natalia Díaz-Rodríguez +5 more
TL;DR: This paper aims at reviewing the existing state of the art of continual learning, summarizing existing benchmarks and metrics, and proposing a framework for presenting and evaluating both robotics and non robotics approaches in a way that makes transfer between both fields easier.