Literatura académica sobre el tema "Post-hoc Explainability"
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Artículos de revistas sobre el tema "Post-hoc Explainability"
Fauvel, 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 completoMochaourab, Rami, Arun Venkitaraman, Isak Samsten, Panagiotis Papapetrou y Cristian R. Rojas. "Post Hoc Explainability for Time Series Classification: Toward a signal processing perspective". IEEE Signal Processing Magazine 39, n.º 4 (julio de 2022): 119–29. http://dx.doi.org/10.1109/msp.2022.3155955.
Texto completoLee, Gin Chong y Chu Kiong Loo. "On the Post Hoc Explainability of Optimized Self-Organizing Reservoir Network for Action Recognition". Sensors 22, n.º 5 (1 de marzo de 2022): 1905. http://dx.doi.org/10.3390/s22051905.
Texto completoMaree, Charl y Christian Omlin. "Reinforcement Learning Your Way: Agent Characterization through Policy Regularization". AI 3, n.º 2 (24 de marzo de 2022): 250–59. http://dx.doi.org/10.3390/ai3020015.
Texto completoYan, Fei, Yunqing Chen, Yiwen Xia, Zhiliang Wang y Ruoxiu Xiao. "An Explainable Brain Tumor Detection Framework for MRI Analysis". Applied Sciences 13, n.º 6 (8 de marzo de 2023): 3438. http://dx.doi.org/10.3390/app13063438.
Texto completoMaarten Schraagen, Jan, Sabin Kerwien Lopez, Carolin Schneider, Vivien Schneider, Stephanie Tönjes y Emma Wiechmann. "The Role of Transparency and Explainability in Automated Systems". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, n.º 1 (septiembre de 2021): 27–31. http://dx.doi.org/10.1177/1071181321651063.
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 completoCho, Hyeoncheol, Youngrock Oh y Eunjoo Jeon. "SEEN: Seen: Sharpening Explanations for Graph Neural Networks Using Explanations From Neighborhoods". Advances in Artificial Intelligence and Machine Learning 03, n.º 02 (2023): 1165–79. http://dx.doi.org/10.54364/aaiml.2023.1168.
Texto completoChatterjee, Soumick, Arnab Das, Chirag Mandal, Budhaditya Mukhopadhyay, Manish Vipinraj, Aniruddh Shukla, Rajatha Nagaraja Rao, Chompunuch Sarasaen, Oliver Speck y Andreas Nürnberger. "TorchEsegeta: Framework for Interpretability and Explainability of Image-Based Deep Learning Models". Applied Sciences 12, n.º 4 (10 de febrero de 2022): 1834. http://dx.doi.org/10.3390/app12041834.
Texto completoRoscher, R., B. Bohn, M. F. Duarte y J. Garcke. "EXPLAIN IT TO ME – FACING REMOTE SENSING CHALLENGES IN THE BIO- AND GEOSCIENCES WITH EXPLAINABLE MACHINE LEARNING". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (3 de agosto de 2020): 817–24. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-817-2020.
Texto completoTesis sobre el tema "Post-hoc Explainability"
Bhattacharya, 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 "Post-hoc 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 completoMoustakidis, Serafeim, Charis Ntakolia, Dimitrios E. Diamantis, Nikolaos Papandrianos y Elpiniki I. Papageorgiou. "Application and post-hoc explainability of deep convolutional neural networks for bone cancer metastasis classification in prostate patients". En Artificial Intelligence in Cancer Diagnosis and Prognosis, Volume 3, 10–1. IOP Publishing, 2022. http://dx.doi.org/10.1088/978-0-7503-3603-1ch10.
Texto completoActas de conferencias sobre el tema "Post-hoc Explainability"
Zhou, Tongyu, Haoyu Sheng y Iris Howley. "Assessing Post-hoc Explainability of the BKT Algorithm". En AIES '20: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3375627.3375856.
Texto completoSaini, Aditya y Ranjitha Prasad. "Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability". En AIES '22: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514094.3534191.
Texto completoKokkotis, Christos, Serafeim Moustakidis, Elpiniki Papageorgiou, Giannis Giakas y Dimitrios Tsaopoulos. "A Machine Learning workflow for Diagnosis of Knee Osteoarthritis with a focus on post-hoc explainability". En 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2020. http://dx.doi.org/10.1109/iisa50023.2020.9284354.
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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