Výsledky vyhledávání - "Arrieta, Alejandro"

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Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization

Autor: Del Ser, Javier, Barredo-Arrieta, Alejandro, Díaz-Rodríguez, Natalia, Herrera, Francisco, Holzinger, Andreas

There is a broad consensus on the importance of deep learning models in tasks involving complex data. Often, an adequate understanding of these models is required when focusing on the transparency of decisions in human-critical applications. Besides

Externí odkaz: http://arxiv.org/abs/2205.10232

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On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification

Autor: Arrieta, Alejandro Barredo, Gil-Lopez, Sergio, Laña, Ibai, Bilbao, Miren Nekane, Del Ser, Javier

Since their inception, learning techniques under the Reservoir Computing paradigm have shown a great modeling capability for recurrent systems without the computing overheads required for other approaches. Among them, different flavors of echo state

Externí odkaz: http://arxiv.org/abs/2102.08634

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Akademický článek

On generating trustworthy counterfactual explanations

Autor: Del Ser, Javier, Barredo-Arrieta, Alejandro, Díaz-Rodríguez, Natalia, Herrera, Francisco, Saranti, Anna, Holzinger, Andreas

Publikováno v: In Information Sciences January 2024 655

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Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples

Autor: Barredo-Arrieta, Alejandro, Del Ser, Javier

The last decade has witnessed the proliferation of Deep Learning models in many applications, achieving unrivaled levels of predictive performance. Unfortunately, the black-box nature of Deep Learning models has posed unanswered questions about what

Externí odkaz: http://arxiv.org/abs/2003.11323

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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

Autor: Arrieta, Alejandro Barredo, Díaz-Rodríguez, Natalia, Del Ser, Javier, Bennetot, Adrien, Tabik, Siham, Barbado, Alberto, García, Salvador, Gil-López, Sergio, Molina, Daniel, Benjamins, Richard, Chatila, Raja, Herrera, Francisco

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explai

Externí odkaz: http://arxiv.org/abs/1910.10045

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