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Publication search results
found 298 matches
- 2024
- Bruno Mota
, Pedro Faria, Juan M. Corchado, Carlos Ramos:
Explainable Artificial Intelligence Applied to Predictive Maintenance: Comparison of Post-Hoc Explainability Techniques. xAI (4) 2024: 353-364 - Carlo Abrate, Federico Siciliano, Francesco Bonchi, Fabrizio Silvestri:
Human-in-the-Loop Personalized Counterfactual Recourse. xAI (3) 2024: 18-38 - Fernando Aguilar-Canto, Omar García-Vázquez, Tania Alcántara, Alberto Espinosa-Juárez, Hiram Calvo:
Associative Interpretability of Hidden Semantics with Contrastiveness Operators in Face Classification Tasks. xAI (1) 2024: 469-491 - Gulsum Alicioglu, Bo Sun:
Use Bag-of-Patterns Approach to Explore Learned Behaviors of Reinforcement Learning. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 41-48 - Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady:
Challenges and Opportunities in Text Generation Explainability. xAI (1) 2024: 244-264 - Jonas Amling, Stephan Scheele, Emanuel Slany, Moritz Lang, Ute Schmid:
Explainable AI for Mixed Data Clustering. xAI (2) 2024: 42-62 - Eric Arazo, Hristo Stoev, Cristian Bosch, Andrés L. Suárez-Cetrulo, Ricardo Simon Carbajo:
Xpression: A Unifying Metric to Optimize Compression and Explainability Robustness of AI Models. xAI (1) 2024: 370-382 - Leonardo Arrighi, Luca Pennella, Gabriel Marques Tavares, Sylvio Barbon Junior:
Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles. xAI (2) 2024: 311-332 - André Artelt, Andreas Gregoriades:
A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanations. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 233-240 - Eduard Barbu, Marharyta Domnich, Raul Vicente, Nikos Sakkas:
Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 153-160 - Jorn-Jan van de Beld, Shreyasi Pathak, Jeroen Geerdink, Johannes H. Hegeman, Christin Seifert:
Feature Importance to Explain Multimodal Prediction Models. a Clinical Use Case. xAI (4) 2024: 84-101 - Laura Bergomi:
Fostering Human-AI interaction: development of a Clinical Decision Support System enhanced by eXplainable AI and Natural Language Processing. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 321-328 - Giovanni Bocchi, Patrizio Frosini, Alessandra Micheletti, Alessandro Pedretti, Gianluca Palermo, Davide Gadioli, Carmen Gratteri, Filippo Lunghini, Andrea Rosario Beccari, Anna Fava, Carmine Talarico:
A geometric XAI approach to protein pocket detection. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 217-224 - Alessio Borriero, Martina Milazzo, Matteo Diano, Davide Orsenigo, Maria-Chiara Villa, Chiara Di Fazio, Marco Tamietto, Alan Perotti:
Explainable Emotion Decoding for Human and Computer Vision. xAI (2) 2024: 178-201 - Nils Ole Breuer, Andreas Sauter, Majid Mohammadi, Erman Acar:
CAGE: Causality-Aware Shapley Value for Global Explanations. xAI (3) 2024: 143-162 - Adrian Byrne:
Pricing Risk: An XAI Analysis of Irish Car Insurance Premiums. xAI (4) 2024: 315-330 - Federico Cabitza, Caterina Fregosi, Andrea Campagner, Chiara Natali:
Explanations Considered Harmful: The Impact of Misleading Explanations on Accuracy in Hybrid Human-AI Decision Making. xAI (4) 2024: 255-269 - Marta Caro-Martínez, Jose L. Jorro-Aragoneses, Belén Díaz-Agudo, Juan A. Recio-García:
Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study. xAI (2) 2024: 28-41 - Marta Caro-Martínez, Anne Liret, Belén Díaz-Agudo, Juan A. Recio-García, Jesus M. Darias, Nirmalie Wiratunga, Anjana Wijekoon, Kyle Martin, Ikechukwu Nkisi-Orji, David Corsar, Chamath Palihawadana, Craig Pirie, Derek G. Bridge, Preeja Pradeep, Bruno Fleisch:
Building Personalised XAI Experiences Through iSee: a Case-Based Reasoning-Driven Platform. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 313-320 - Alessandro Castelnovo, Roberto Depalmas, Fabio Mercorio, Nicolò Mombelli, Daniele Potertì, Antonio Serino, Andrea Seveso, Salvatore Sorrentino, Laura Viola:
Augmenting XAI with LLMs: A Case Study in Banking Marketing Recommendation. xAI (1) 2024: 211-229 - Teodor Chiaburu, Frank Haußer, Felix Bießmann:
CoProNN: Concept-Based Prototypical Nearest Neighbors for Explaining Vision Models. xAI (1) 2024: 69-91 - Arjun Vinayak Chikkankod, Luca Longo:
A proposal for improving EEG microstate generation via interpretable deep clustering with convolutional autoencoders. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 25-32 - Martina Cinquini, Riccardo Guidotti:
Causality-Aware Local Interpretable Model-Agnostic Explanations. xAI (3) 2024: 108-124 - Riccardo Crupi, Daniele Regoli, Alessandro Damiano Sabatino, Immacolata Marano, Massimiliano Brinis, Luca Albertazzi, Andrea Cirillo, Andrea Claudio Cosentini:
Unraveling Anomalies: Explaining Outliers with DTOR. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 257-264 - Jane Arleth dela Cruz, Iris Hendrickx, Martha A. Larson:
NoNE Found: Explaining the Output of Sequence-to-Sequence Models When No Named Entity Is Recognized. xAI (1) 2024: 265-284 - Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann:
mlr3summary: Concise and interpretable summaries for machine learning models. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 281-288 - Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright:
CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests. xAI (3) 2024: 85-107 - Danilo Danese:
Optimizing Synthetic Data from Scarcity: Towards Meaningful Data Generation in High-Dimensional Low-Sample Size Domains. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 329-336 - Oleksandr Davydko:
Assesing the Interpretability of the Statistical Radiomic Features via Image Saliency Maps in Medical Image Classification Tasks. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 337-344 - Oleksandr Davydko, Vladimir Pavlov, Przemyslaw Biecek, Luca Longo:
SRFAMap: A Method for Mapping Integrated Gradients of a CNN Trained with Statistical Radiomic Features to Medical Image Saliency Maps. xAI (4) 2024: 3-23
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