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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

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Cited by:

  1. James V. Hansen, 2021. "Coalition Feature Interpretation and Attribution in Algorithmic Trading Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 849-866, October.
  2. Damiano Brigo & Xiaoshan Huang & Andrea Pallavicini & Haitz Saez de Ocariz Borde, 2021. "Interpretability in deep learning for finance: a case study for the Heston model," Papers 2104.09476, arXiv.org.
  3. Jana Gerlach & Paul Hoppe & Sarah Jagels & Luisa Licker & Michael H. Breitner, 2022. "Decision support for efficient XAI services - A morphological analysis, business model archetypes, and a decision tree," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2139-2158, December.
  4. Vaishali U. Gongane & Mousami V. Munot & Alwin D. Anuse, 2024. "A survey of explainable AI techniques for detection of fake news and hate speech on social media platforms," Journal of Computational Social Science, Springer, vol. 7(1), pages 587-623, April.
  5. Sherwan Mohammed Najm & Imre Paniti, 2023. "Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 331-367, January.
  6. Abdulrashid, Ismail & Zanjirani Farahani, Reza & Mammadov, Shamkhal & Khalafalla, Mohamed & Chiang, Wen-Chyuan, 2024. "Explainable artificial intelligence in transport Logistics: Risk analysis for road accidents," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
  7. Chicchi, Lorenzo & Giambagli, Lorenzo & Buffoni, Lorenzo & Marino, Raffaele & Fanelli, Duccio, 2024. "Complex Recurrent Spectral Network," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
  8. Yoonjae Noh & Jong-Min Kim & Soongoo Hong & Sangjin Kim, 2023. "Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
  9. Mark Gromowski & Michael Siebers & Ute Schmid, 2020. "A process framework for inducing and explaining Datalog theories," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 821-835, December.
  10. Kunal Pattanayak & Vikram Krishnamurthy, 2021. "Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification," Papers 2102.04594, arXiv.org, revised Jul 2021.
  11. Gabriel Ferrettini & Elodie Escriva & Julien Aligon & Jean-Baptiste Excoffier & Chantal Soulé-Dupuy, 2022. "Coalitional Strategies for Efficient Individual Prediction Explanation," Information Systems Frontiers, Springer, vol. 24(1), pages 49-75, February.
  12. Fallahgoul, Hasan & Franstianto, Vincentius & Lin, Xin, 2024. "Asset pricing with neural networks: Significance tests," Journal of Econometrics, Elsevier, vol. 238(1).
  13. Davazdahemami, Behrooz & Kalgotra, Pankush & Zolbanin, Hamed M. & Delen, Dursun, 2023. "A developer-oriented recommender model for the app store: A predictive network analytics approach," Journal of Business Research, Elsevier, vol. 158(C).
  14. Kevin Fauvel & Tao Lin & Véronique Masson & Élisa Fromont & Alexandre Termier, 2021. "XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification," Mathematics, MDPI, vol. 9(23), pages 1-19, December.
  15. Parmar, Janak & Das, Pritikana & Dave, Sanjaykumar M., 2021. "A machine learning approach for modelling parking duration in urban land-use," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
  16. Amini, Mostafa & Bagheri, Ali & Delen, Dursun, 2022. "Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
  17. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
  18. Lara Marie Demajo & Vince Vella & Alexiei Dingli, 2020. "Explainable AI for Interpretable Credit Scoring," Papers 2012.03749, arXiv.org.
  19. André Steimers & Moritz Schneider, 2022. "Sources of Risk of AI Systems," IJERPH, MDPI, vol. 19(6), pages 1-32, March.
  20. Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023. "A Comprehensive Review on Financial Explainable AI," Papers 2309.11960, arXiv.org.
  21. Bo Yuan & Damiano Brigo & Antoine Jacquier & Nicola Pede, 2024. "Deep learning interpretability for rough volatility," Papers 2411.19317, arXiv.org.
  22. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
  23. Lars Ole Hjelkrem & Petter Eilif de Lange, 2023. "Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data," JRFM, MDPI, vol. 16(4), pages 1-19, April.
  24. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.
  25. Pelin Ayranci & Phung Lai & Nhathai Phan & Han Hu & Alexander Kolinowski & David Newman & Deijing Dou, 2022. "OnML: an ontology-based approach for interpretable machine learning," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 770-793, August.
  26. Zhu, Alex Yue Feng, 2024. "Optimizing financial decision-making for emerging adults: A compact Python-based personalized financial projection approach," Technology in Society, Elsevier, vol. 77(C).
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