Literatura académica sobre el tema "Model-agnostic Explainability"
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Artículos de revistas sobre el tema "Model-agnostic Explainability"
Diprose, William K., Nicholas Buist, Ning Hua, Quentin Thurier, George Shand y Reece Robinson. "Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator". Journal of the American Medical Informatics Association 27, n.º 4 (27 de febrero de 2020): 592–600. http://dx.doi.org/10.1093/jamia/ocz229.
Texto completoZafar, Muhammad Rehman y Naimul Khan. "Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability". Machine Learning and Knowledge Extraction 3, n.º 3 (30 de junio de 2021): 525–41. http://dx.doi.org/10.3390/make3030027.
Texto completoTOPCU, Deniz. "How to explain a machine learning model: HbA1c classification example". Journal of Medicine and Palliative Care 4, n.º 2 (27 de marzo de 2023): 117–25. http://dx.doi.org/10.47582/jompac.1259507.
Texto completoUllah, Ihsan, Andre Rios, Vaibhav Gala y Susan Mckeever. "Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation". Applied Sciences 12, n.º 1 (23 de diciembre de 2021): 136. http://dx.doi.org/10.3390/app12010136.
Texto completoSrinivasu, Parvathaneni Naga, N. Sandhya, Rutvij H. Jhaveri y Roshani Raut. "From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies". Mobile Information Systems 2022 (13 de junio de 2022): 1–20. http://dx.doi.org/10.1155/2022/8167821.
Texto completoLv, Ge, Chen Jason Zhang y Lei Chen. "HENCE-X: Toward Heterogeneity-Agnostic Multi-Level Explainability for Deep Graph Networks". Proceedings of the VLDB Endowment 16, n.º 11 (julio de 2023): 2990–3003. http://dx.doi.org/10.14778/3611479.3611503.
Texto completoFauvel, Kevin, Tao Lin, Véronique Masson, Élisa Fromont y Alexandre Termier. "XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification". Mathematics 9, n.º 23 (5 de diciembre de 2021): 3137. http://dx.doi.org/10.3390/math9233137.
Texto completoHassan, Fayaz, Jianguo Yu, Zafi Sherhan Syed, Nadeem Ahmed, Mana Saleh Al Reshan y Asadullah Shaikh. "Achieving model explainability for intrusion detection in VANETs with LIME". PeerJ Computer Science 9 (22 de junio de 2023): e1440. http://dx.doi.org/10.7717/peerj-cs.1440.
Texto completoVieira, Carla Piazzon Ramos y Luciano Antonio Digiampietri. "A study about Explainable Articial Intelligence: using decision tree to explain SVM". Revista Brasileira de Computação Aplicada 12, n.º 1 (8 de enero de 2020): 113–21. http://dx.doi.org/10.5335/rbca.v12i1.10247.
Texto completoNguyen, Hung Viet y Haewon Byeon. "Prediction of Out-of-Hospital Cardiac Arrest Survival Outcomes Using a Hybrid Agnostic Explanation TabNet Model". Mathematics 11, n.º 9 (25 de abril de 2023): 2030. http://dx.doi.org/10.3390/math11092030.
Texto completoTesis sobre el tema "Model-agnostic Explainability"
Stanzione, Vincenzo Maria. "Developing a new approach for machine learning explainability combining local and global model-agnostic approaches". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25480/.
Texto completoBhattacharya, Debarpan. "A Learnable Distillation Approach For Model-agnostic Explainability With Multimodal Applications". Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6108.
Texto completoCapítulos de libros sobre el tema "Model-agnostic Explainability"
Stevens, Alexander, Johannes De Smedt y Jari Peeperkorn. "Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring". En Lecture Notes in Business Information Processing, 194–206. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_15.
Texto completoBaniecki, Hubert, Wojciech Kretowicz y Przemyslaw Biecek. "Fooling Partial Dependence via Data Poisoning". En Machine Learning and Knowledge Discovery in Databases, 121–36. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_8.
Texto completoSovrano, Francesco, Salvatore Sapienza, Monica Palmirani y Fabio Vitali. "A Survey on Methods and Metrics for the Assessment of Explainability Under the Proposed AI Act". En Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210342.
Texto completoActas de conferencias sobre el tema "Model-agnostic Explainability"
Prentzas, Nicoletta, Marios Pitsiali, Efthyvoulos Kyriacou, Andrew Nicolaides, Antonis Kakas y Constantinos S. Pattichis. "Model Agnostic Explainability Techniques in Ultrasound Image Analysis". En 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2021. http://dx.doi.org/10.1109/bibe52308.2021.9635199.
Texto completoHamilton, Nicholas, Adam Webb, Matt Wilder, Ben Hendrickson, Matt Blanck, Erin Nelson, Wiley Roemer y Timothy C. Havens. "Enhancing Visualization and Explainability of Computer Vision Models with Local Interpretable Model-Agnostic Explanations (LIME)". En 2022 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2022. http://dx.doi.org/10.1109/ssci51031.2022.10022096.
Texto completoProtopapadakis, Giorgois, Asteris Apostolidis y Anestis I. Kalfas. "Explainable and Interpretable AI-Assisted Remaining Useful Life Estimation for Aeroengines". En ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-80777.
Texto completoStang, Marco, Marc Schindewolf y Eric Sax. "Unraveling Scenario-Based Behavior of a Self-Learning Function with User Interaction". En 10th International Conference on Human Interaction and Emerging Technologies (IHIET 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004028.
Texto completoHaid, Charlotte, Alicia Lang y Johannes Fottner. "Explaining algorithmic decisions: design guidelines for explanations in User Interfaces". En 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003764.
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