Статті в журналах з теми "Post-hoc Explainability"
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Fauvel, Kevin, Tao Lin, Véronique Masson, Élisa Fromont, and Alexandre Termier. "XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification." Mathematics 9, no. 23 (December 5, 2021): 3137. http://dx.doi.org/10.3390/math9233137.
Повний текст джерелаMochaourab, Rami, Arun Venkitaraman, Isak Samsten, Panagiotis Papapetrou, and Cristian R. Rojas. "Post Hoc Explainability for Time Series Classification: Toward a signal processing perspective." IEEE Signal Processing Magazine 39, no. 4 (July 2022): 119–29. http://dx.doi.org/10.1109/msp.2022.3155955.
Повний текст джерелаLee, Gin Chong, and Chu Kiong Loo. "On the Post Hoc Explainability of Optimized Self-Organizing Reservoir Network for Action Recognition." Sensors 22, no. 5 (March 1, 2022): 1905. http://dx.doi.org/10.3390/s22051905.
Повний текст джерелаMaree, Charl, and Christian Omlin. "Reinforcement Learning Your Way: Agent Characterization through Policy Regularization." AI 3, no. 2 (March 24, 2022): 250–59. http://dx.doi.org/10.3390/ai3020015.
Повний текст джерелаYan, Fei, Yunqing Chen, Yiwen Xia, Zhiliang Wang, and Ruoxiu Xiao. "An Explainable Brain Tumor Detection Framework for MRI Analysis." Applied Sciences 13, no. 6 (March 8, 2023): 3438. http://dx.doi.org/10.3390/app13063438.
Повний текст джерелаMaarten Schraagen, Jan, Sabin Kerwien Lopez, Carolin Schneider, Vivien Schneider, Stephanie Tönjes, and Emma Wiechmann. "The Role of Transparency and Explainability in Automated Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (September 2021): 27–31. http://dx.doi.org/10.1177/1071181321651063.
Повний текст джерелаSrinivasu, Parvathaneni Naga, N. Sandhya, Rutvij H. Jhaveri, and Roshani Raut. "From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies." Mobile Information Systems 2022 (June 13, 2022): 1–20. http://dx.doi.org/10.1155/2022/8167821.
Повний текст джерелаCho, Hyeoncheol, Youngrock Oh, and Eunjoo Jeon. "SEEN: Seen: Sharpening Explanations for Graph Neural Networks Using Explanations From Neighborhoods." Advances in Artificial Intelligence and Machine Learning 03, no. 02 (2023): 1165–79. http://dx.doi.org/10.54364/aaiml.2023.1168.
Повний текст джерелаChatterjee, Soumick, Arnab Das, Chirag Mandal, Budhaditya Mukhopadhyay, Manish Vipinraj, Aniruddh Shukla, Rajatha Nagaraja Rao, Chompunuch Sarasaen, Oliver Speck, and Andreas Nürnberger. "TorchEsegeta: Framework for Interpretability and Explainability of Image-Based Deep Learning Models." Applied Sciences 12, no. 4 (February 10, 2022): 1834. http://dx.doi.org/10.3390/app12041834.
Повний текст джерелаRoscher, R., B. Bohn, M. F. Duarte, and 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 (August 3, 2020): 817–24. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-817-2020.
Повний текст джерелаApostolopoulos, Ioannis D., Ifigeneia Athanasoula, Mpesi Tzani, and Peter P. Groumpos. "An Explainable Deep Learning Framework for Detecting and Localising Smoke and Fire Incidents: Evaluation of Grad-CAM++ and LIME." Machine Learning and Knowledge Extraction 4, no. 4 (December 6, 2022): 1124–35. http://dx.doi.org/10.3390/make4040057.
Повний текст джерелаAntoniadi, Anna Markella, Miriam Galvin, Mark Heverin, Lan Wei, Orla Hardiman, and Catherine Mooney. "A Clinical Decision Support System for the Prediction of Quality of Life in ALS." Journal of Personalized Medicine 12, no. 3 (March 10, 2022): 435. http://dx.doi.org/10.3390/jpm12030435.
Повний текст джерелаMoustakidis, Serafeim, Christos Kokkotis, Dimitrios Tsaopoulos, Petros Sfikakis, Sotirios Tsiodras, Vana Sypsa, Theoklis E. Zaoutis, and Dimitrios Paraskevis. "Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning." Viruses 14, no. 3 (March 17, 2022): 625. http://dx.doi.org/10.3390/v14030625.
Повний текст джерелаSudars, Kaspars, Ivars Namatēvs, and Kaspars Ozols. "Improving Performance of the PRYSTINE Traffic Sign Classification by Using a Perturbation-Based Explainability Approach." Journal of Imaging 8, no. 2 (January 30, 2022): 30. http://dx.doi.org/10.3390/jimaging8020030.
Повний текст джерелаHong, Jung-Ho, Woo-Jeoung Nam, Kyu-Sung Jeon, and Seong-Whan Lee. "Towards Better Visualizing the Decision Basis of Networks via Unfold and Conquer Attribution Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 7884–92. http://dx.doi.org/10.1609/aaai.v37i7.25954.
Повний текст джерелаSingh, Rajeev Kumar, Rohan Gorantla, Sai Giridhar Rao Allada, and Pratap Narra. "SkiNet: A deep learning framework for skin lesion diagnosis with uncertainty estimation and explainability." PLOS ONE 17, no. 10 (October 31, 2022): e0276836. http://dx.doi.org/10.1371/journal.pone.0276836.
Повний текст джерелаNtakolia, Charis, Christos Kokkotis, Patrik Karlsson, and Serafeim Moustakidis. "An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management." Sensors 21, no. 23 (November 27, 2021): 7926. http://dx.doi.org/10.3390/s21237926.
Повний текст джерелаVieira, Carla Piazzon Ramos, and Luciano Antonio Digiampietri. "A study about Explainable Articial Intelligence: using decision tree to explain SVM." Revista Brasileira de Computação Aplicada 12, no. 1 (January 8, 2020): 113–21. http://dx.doi.org/10.5335/rbca.v12i1.10247.
Повний текст джерелаMaree, Charl, and Christian W. Omlin. "Can Interpretable Reinforcement Learning Manage Prosperity Your Way?" AI 3, no. 2 (June 13, 2022): 526–37. http://dx.doi.org/10.3390/ai3020030.
Повний текст джерелаJ. Thiagarajan, Jayaraman, Vivek Narayanaswamy, Rushil Anirudh, Peer-Timo Bremer, and Andreas Spanias. "Accurate and Robust Feature Importance Estimation under Distribution Shifts." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7891–98. http://dx.doi.org/10.1609/aaai.v35i9.16963.
Повний текст джерелаRguibi, Zakaria, Abdelmajid Hajami, Dya Zitouni, Amine Elqaraoui, and Anas Bedraoui. "CXAI: Explaining Convolutional Neural Networks for Medical Imaging Diagnostic." Electronics 11, no. 11 (June 2, 2022): 1775. http://dx.doi.org/10.3390/electronics11111775.
Повний текст джерелаXu, Qian, Wenzhao Xie, Bolin Liao, Chao Hu, Lu Qin, Zhengzijin Yang, Huan Xiong, Yi Lyu, Yue Zhou, and Aijing Luo. "Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review." Journal of Healthcare Engineering 2023 (February 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/9919269.
Повний текст джерелаBai, Xi, Zhibo Zhou, Yunyun Luo, Hongbo Yang, Huijuan Zhu, Shi Chen, and Hui Pan. "Development and Evaluation of a Machine Learning Prediction Model for Small-for-Gestational-Age Births in Women Exposed to Radiation before Pregnancy." Journal of Personalized Medicine 12, no. 4 (March 31, 2022): 550. http://dx.doi.org/10.3390/jpm12040550.
Повний текст джерелаAntoniadi, Anna Markella, Yuhan Du, Yasmine Guendouz, Lan Wei, Claudia Mazo, Brett A. Becker, and Catherine Mooney. "Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review." Applied Sciences 11, no. 11 (May 31, 2021): 5088. http://dx.doi.org/10.3390/app11115088.
Повний текст джерелаArnold, Thomas, Daniel Kasenberg, and Matthias Scheutz. "Explaining in Time." ACM Transactions on Human-Robot Interaction 10, no. 3 (July 2021): 1–23. http://dx.doi.org/10.1145/3457183.
Повний текст джерелаHamm, Pascal, Michael Klesel, Patricia Coberger, and H. Felix Wittmann. "Explanation matters: An experimental study on explainable AI." Electronic Markets 33, no. 1 (May 10, 2023). http://dx.doi.org/10.1007/s12525-023-00640-9.
Повний текст джерелаLenatti, Marta, Pedro A. Moreno-Sánchez, Edoardo M. Polo, Maximiliano Mollura, Riccardo Barbieri, and Alessia Paglialonga. "Evaluation of Machine Learning Algorithms and Explainability Techniques to Detect Hearing Loss From a Speech-in-Noise Screening Test." American Journal of Audiology, July 25, 2022, 1–19. http://dx.doi.org/10.1044/2022_aja-21-00194.
Повний текст джерелаMoreno-Sánchez, Pedro A. "Improvement of a prediction model for heart failure survival through explainable artificial intelligence." Frontiers in Cardiovascular Medicine 10 (August 1, 2023). http://dx.doi.org/10.3389/fcvm.2023.1219586.
Повний текст джерелаVale, Daniel, Ali El-Sharif, and Muhammed Ali. "Explainable artificial intelligence (XAI) post-hoc explainability methods: risks and limitations in non-discrimination law." AI and Ethics, March 15, 2022. http://dx.doi.org/10.1007/s43681-022-00142-y.
Повний текст джерелаCorbin, Adam, and Oge Marques. "Assessing Bias in Skin Lesion Classifiers with Contemporary Deep Learning and Post-Hoc Explainability Techniques." IEEE Access, 2023, 1. http://dx.doi.org/10.1109/access.2023.3289320.
Повний текст джерелаDervakos, Edmund, Natalia Kotsani, and Giorgos Stamou. "Genre Recognition from Symbolic Music with CNNs: Performance and Explainability." SN Computer Science 4, no. 2 (December 17, 2022). http://dx.doi.org/10.1007/s42979-022-01490-6.
Повний текст джерелаPapagni, Guglielmo, Jesse de Pagter, Setareh Zafari, Michael Filzmoser, and Sabine T. Koeszegi. "Artificial agents’ explainability to support trust: considerations on timing and context." AI & SOCIETY, June 27, 2022. http://dx.doi.org/10.1007/s00146-022-01462-7.
Повний текст джерелаBarredo Arrieta, Alejandro, Sergio Gil-Lopez, Ibai Laña, Miren Nekane Bilbao, and Javier Del Ser. "On the post-hoc explainability of deep echo state networks for time series forecasting, image and video classification." Neural Computing and Applications, August 6, 2021. http://dx.doi.org/10.1007/s00521-021-06359-y.
Повний текст джерелаSharma, Jeetesh, Murari Lal Mittal, Gunjan Soni, and Arvind Keprate. "Explainable Artificial Intelligence (XAI) Approaches in Predictive Maintenance: A Review." Recent Patents on Engineering 18 (April 17, 2023). http://dx.doi.org/10.2174/1872212118666230417084231.
Повний текст джерелаVilone, Giulia, and Luca Longo. "A Quantitative Evaluation of Global, Rule-Based Explanations of Post-Hoc, Model Agnostic Methods." Frontiers in Artificial Intelligence 4 (November 3, 2021). http://dx.doi.org/10.3389/frai.2021.717899.
Повний текст джерелаZini, Julia El, and Mariette Awad. "On the Explainability of Natural Language Processing Deep Models." ACM Computing Surveys, July 19, 2022. http://dx.doi.org/10.1145/3529755.
Повний текст джерелаWeber, Patrick, K. Valerie Carl, and Oliver Hinz. "Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature." Management Review Quarterly, February 28, 2023. http://dx.doi.org/10.1007/s11301-023-00320-0.
Повний текст джерелаKarim, Muhammad Monjurul, Yu Li, and Ruwen Qin. "Toward Explainable Artificial Intelligence for Early Anticipation of Traffic Accidents." Transportation Research Record: Journal of the Transportation Research Board, February 18, 2022, 036119812210761. http://dx.doi.org/10.1177/03611981221076121.
Повний текст джерелаBaptista, Delora, João Correia, Bruno Pereira, and Miguel Rocha. "Evaluating molecular representations in machine learning models for drug response prediction and interpretability." Journal of Integrative Bioinformatics, August 26, 2022. http://dx.doi.org/10.1515/jib-2022-0006.
Повний текст джерелаNorton, Adam, Henny Admoni, Jacob Crandall, Tesca Fitzgerald, Alvika Gautam, Michael Goodrich, Amy Saretsky, et al. "Metrics for Robot Proficiency Self-Assessment and Communication of Proficiency in Human-Robot Teams." ACM Transactions on Human-Robot Interaction, April 14, 2022. http://dx.doi.org/10.1145/3522579.
Повний текст джерелаKucklick, Jan-Peter, and Oliver Müller. "Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal." ACM Transactions on Management Information Systems, October 10, 2022. http://dx.doi.org/10.1145/3567430.
Повний текст джерелаFleisher, Will. "Understanding, Idealization, and Explainable AI." Episteme, November 3, 2022, 1–27. http://dx.doi.org/10.1017/epi.2022.39.
Повний текст джерелаChen, Ruoyu, Jingzhi Li, Hua Zhang, Changchong Sheng, Li Liu, and Xiaochun Cao. "Sim2Word: Explaining Similarity with Representative Attribute Words via Counterfactual Explanations." ACM Transactions on Multimedia Computing, Communications, and Applications, September 8, 2022. http://dx.doi.org/10.1145/3563039.
Повний текст джерелаSamarasinghe, Dilini. "Counterfactual learning in enhancing resilience in autonomous agent systems." Frontiers in Artificial Intelligence 6 (July 28, 2023). http://dx.doi.org/10.3389/frai.2023.1212336.
Повний текст джерелаBelle, Vaishak, and Ioannis Papantonis. "Principles and Practice of Explainable Machine Learning." Frontiers in Big Data 4 (July 1, 2021). http://dx.doi.org/10.3389/fdata.2021.688969.
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