Artículos de revistas sobre el tema "Interpretable methods"
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Topin, Nicholay, Stephanie Milani, Fei Fang y Manuela Veloso. "Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de mayo de 2021): 9923–31. http://dx.doi.org/10.1609/aaai.v35i11.17192.
Texto completoKATAOKA, Makoto. "COMPUTER-INTERPRETABLE DESCRIPTION OF CONSTRUCTION METHODS". AIJ Journal of Technology and Design 13, n.º 25 (2007): 277–80. http://dx.doi.org/10.3130/aijt.13.277.
Texto completoMurdoch, W. James, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl y Bin Yu. "Definitions, methods, and applications in interpretable machine learning". Proceedings of the National Academy of Sciences 116, n.º 44 (16 de octubre de 2019): 22071–80. http://dx.doi.org/10.1073/pnas.1900654116.
Texto completoAlangari, Nourah, Mohamed El Bachir Menai, Hassan Mathkour y Ibrahim Almosallam. "Exploring Evaluation Methods for Interpretable Machine Learning: A Survey". Information 14, n.º 8 (21 de agosto de 2023): 469. http://dx.doi.org/10.3390/info14080469.
Texto completoKenesei, Tamás y János Abonyi. "Interpretable support vector regression". Artificial Intelligence Research 1, n.º 2 (9 de octubre de 2012): 11. http://dx.doi.org/10.5430/air.v1n2p11.
Texto completoYe, Zhuyifan, Wenmian Yang, Yilong Yang y Defang Ouyang. "Interpretable machine learning methods for in vitro pharmaceutical formulation development". Food Frontiers 2, n.º 2 (5 de mayo de 2021): 195–207. http://dx.doi.org/10.1002/fft2.78.
Texto completoMi, Jian-Xun, An-Di Li y Li-Fang Zhou. "Review Study of Interpretation Methods for Future Interpretable Machine Learning". IEEE Access 8 (2020): 191969–85. http://dx.doi.org/10.1109/access.2020.3032756.
Texto completoObermann, Lennart y Stephan Waack. "Demonstrating non-inferiority of easy interpretable methods for insolvency prediction". Expert Systems with Applications 42, n.º 23 (diciembre de 2015): 9117–28. http://dx.doi.org/10.1016/j.eswa.2015.08.009.
Texto completoAssegie, Tsehay Admassu. "Evaluation of the Shapley Additive Explanation Technique for Ensemble Learning Methods". Proceedings of Engineering and Technology Innovation 21 (22 de abril de 2022): 20–26. http://dx.doi.org/10.46604/peti.2022.9025.
Texto completoBang, Seojin, Pengtao Xie, Heewook Lee, Wei Wu y Eric Xing. "Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 13 (18 de mayo de 2021): 11396–404. http://dx.doi.org/10.1609/aaai.v35i13.17358.
Texto completoLi, Qiaomei, Rachel Cummings y Yonatan Mintz. "Optimal Local Explainer Aggregation for Interpretable Prediction". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junio de 2022): 12000–12007. http://dx.doi.org/10.1609/aaai.v36i11.21458.
Texto completoMahya, Parisa y Johannes Fürnkranz. "An Empirical Comparison of Interpretable Models to Post-Hoc Explanations". AI 4, n.º 2 (19 de mayo de 2023): 426–36. http://dx.doi.org/10.3390/ai4020023.
Texto completoLee, Franklin Langlang, Jaehong Park, Sushmit Goyal, Yousef Qaroush, Shihu Wang, Hong Yoon, Aravind Rammohan y Youngseon Shim. "Comparison of Machine Learning Methods towards Developing Interpretable Polyamide Property Prediction". Polymers 13, n.º 21 (23 de octubre de 2021): 3653. http://dx.doi.org/10.3390/polym13213653.
Texto completoLi, Xiao, Zachary Serlin, Guang Yang y Calin Belta. "A formal methods approach to interpretable reinforcement learning for robotic planning". Science Robotics 4, n.º 37 (18 de diciembre de 2019): eaay6276. http://dx.doi.org/10.1126/scirobotics.aay6276.
Texto completoSkirzyński, Julian, Frederic Becker y Falk Lieder. "Automatic discovery of interpretable planning strategies". Machine Learning 110, n.º 9 (9 de abril de 2021): 2641–83. http://dx.doi.org/10.1007/s10994-021-05963-2.
Texto completoXu, Yixiao, Xiaolei Liu, Kangyi Ding y Bangzhou Xin. "IBD: An Interpretable Backdoor-Detection Method via Multivariate Interactions". Sensors 22, n.º 22 (10 de noviembre de 2022): 8697. http://dx.doi.org/10.3390/s22228697.
Texto completoGu, Jindong. "Interpretable Graph Capsule Networks for Object Recognition". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 2 (18 de mayo de 2021): 1469–77. http://dx.doi.org/10.1609/aaai.v35i2.16237.
Texto completoHagerty, C. G. y F. A. Sonnenberg. "Computer-Interpretable Clinical Practice Guidelines". Yearbook of Medical Informatics 15, n.º 01 (agosto de 2006): 145–58. http://dx.doi.org/10.1055/s-0038-1638486.
Texto completoGe, Xiaoyi, Mingshu Zhang, Xu An Wang, Jia Liu y Bin Wei. "Emotion-Drive Interpretable Fake News Detection". International Journal of Data Warehousing and Mining 18, n.º 1 (1 de enero de 2022): 1–17. http://dx.doi.org/10.4018/ijdwm.314585.
Texto completoH. Merritt, Sean y Alexander P. Christensen. "An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics". Advances in Artificial Intelligence and Machine Learning 03, n.º 01 (2023): 760–77. http://dx.doi.org/10.54364/aaiml.2023.1149.
Texto completoLuan, Tao, Guoqing Liang y Pengfei Peng. "Interpretable DeepFake Detection Based on Frequency Spatial Transformer". International Journal of Emerging Technologies and Advanced Applications 1, n.º 2 (26 de marzo de 2024): 19–25. http://dx.doi.org/10.62677/ijetaa.2402108.
Texto completoVerma, Abhinav. "Verifiable and Interpretable Reinforcement Learning through Program Synthesis". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 9902–3. http://dx.doi.org/10.1609/aaai.v33i01.33019902.
Texto completoTulsani, Vijya, Prashant Sahatiya, Jignasha Parmar y Jayshree Parmar. "XAI Applications in Medical Imaging: A Survey of Methods and Challenges". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 9 (27 de octubre de 2023): 181–86. http://dx.doi.org/10.17762/ijritcc.v11i9.8332.
Texto completoHayes, Sean M. S., Jeffrey R. Sachs y Carolyn R. Cho. "From complex data to biological insight: ‘DEKER’ feature selection and network inference". Journal of Pharmacokinetics and Pharmacodynamics 49, n.º 1 (17 de noviembre de 2021): 81–99. http://dx.doi.org/10.1007/s10928-021-09792-7.
Texto completoKhakabimamaghani, Sahand, Yogeshwar D. Kelkar, Bruno M. Grande, Ryan D. Morin, Martin Ester y Daniel Ziemek. "SUBSTRA: Supervised Bayesian Patient Stratification". Bioinformatics 35, n.º 18 (15 de febrero de 2019): 3263–72. http://dx.doi.org/10.1093/bioinformatics/btz112.
Texto completoSun, Lili, Xueyan Liu, Min Zhao y Bo Yang. "Interpretable Variational Graph Autoencoder with Noninformative Prior". Future Internet 13, n.º 2 (18 de febrero de 2021): 51. http://dx.doi.org/10.3390/fi13020051.
Texto completoCansel, Neslihan, Fatma Hilal Yagin, Mustafa Akan y Bedriye Ilkay Aygul. "INTERPRETABLE ESTIMATION OF SUICIDE RISK AND SEVERITY FROM COMPLETE BLOOD COUNT PARAMETERS WITH EXPLAINABLE ARTIFICIAL INTELLIGENCE METHODS". PSYCHIATRIA DANUBINA 35, n.º 1 (13 de abril de 2023): 62–72. http://dx.doi.org/10.24869/psyd.2023.62.
Texto completoShi, Liushuai, Le Wang, Chengjiang Long, Sanping Zhou, Fang Zheng, Nanning Zheng y Gang Hua. "Social Interpretable Tree for Pedestrian Trajectory Prediction". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 2 (28 de junio de 2022): 2235–43. http://dx.doi.org/10.1609/aaai.v36i2.20121.
Texto completoKrumb, Henry, Dhritimaan Das, Romol Chadda y Anirban Mukhopadhyay. "CycleGAN for interpretable online EMT compensation". International Journal of Computer Assisted Radiology and Surgery 16, n.º 5 (14 de marzo de 2021): 757–65. http://dx.doi.org/10.1007/s11548-021-02324-1.
Texto completoBhambhoria, Rohan, Hui Liu, Samuel Dahan y Xiaodan Zhu. "Interpretable Low-Resource Legal Decision Making". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junio de 2022): 11819–27. http://dx.doi.org/10.1609/aaai.v36i11.21438.
Texto completoWhiteway, Matthew R., Dan Biderman, Yoni Friedman, Mario Dipoppa, E. Kelly Buchanan, Anqi Wu, John Zhou et al. "Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders". PLOS Computational Biology 17, n.º 9 (22 de septiembre de 2021): e1009439. http://dx.doi.org/10.1371/journal.pcbi.1009439.
Texto completoWalter, Nils Philipp, Jonas Fischer y Jilles Vreeken. "Finding Interpretable Class-Specific Patterns through Efficient Neural Search". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 8 (24 de marzo de 2024): 9062–70. http://dx.doi.org/10.1609/aaai.v38i8.28756.
Texto completoMeng, Fan. "Creating Interpretable Data-Driven Approaches for Tropical Cyclones Forecasting". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junio de 2022): 12892–93. http://dx.doi.org/10.1609/aaai.v36i11.21583.
Texto completoWang, Min, Steven M. Kornblau y Kevin R. Coombes. "Decomposing the Apoptosis Pathway Into Biologically Interpretable Principal Components". Cancer Informatics 17 (1 de enero de 2018): 117693511877108. http://dx.doi.org/10.1177/1176935118771082.
Texto completoWeiss, S. M. y N. Indurkhya. "Rule-based Machine Learning Methods for Functional Prediction". Journal of Artificial Intelligence Research 3 (1 de diciembre de 1995): 383–403. http://dx.doi.org/10.1613/jair.199.
Texto completoFeng, Aosong, Chenyu You, Shiqiang Wang y Leandros Tassiulas. "KerGNNs: Interpretable Graph Neural Networks with Graph Kernels". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 6 (28 de junio de 2022): 6614–22. http://dx.doi.org/10.1609/aaai.v36i6.20615.
Texto completoWang, Yulong, Xiaolu Zhang, Xiaolin Hu, Bo Zhang y Hang Su. "Dynamic Network Pruning with Interpretable Layerwise Channel Selection". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 6299–306. http://dx.doi.org/10.1609/aaai.v34i04.6098.
Texto completoHase, Peter, Chaofan Chen, Oscar Li y Cynthia Rudin. "Interpretable Image Recognition with Hierarchical Prototypes". Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 (28 de octubre de 2019): 32–40. http://dx.doi.org/10.1609/hcomp.v7i1.5265.
Texto completoGhanem, Souhila, Raphaël Couturier y Pablo Gregori. "An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote". Mathematics 9, n.º 12 (8 de junio de 2021): 1315. http://dx.doi.org/10.3390/math9121315.
Texto completoEiras-Franco, Carlos, Bertha Guijarro-Berdiñas, Amparo Alonso-Betanzos y Antonio Bahamonde. "Interpretable Market Segmentation on High Dimension Data". Proceedings 2, n.º 18 (17 de septiembre de 2018): 1171. http://dx.doi.org/10.3390/proceedings2181171.
Texto completoPulkkinen, Pietari y Hannu Koivisto. "Identification of interpretable and accurate fuzzy classifiers and function estimators with hybrid methods". Applied Soft Computing 7, n.º 2 (marzo de 2007): 520–33. http://dx.doi.org/10.1016/j.asoc.2006.11.001.
Texto completoLiu, Yuekai, Tianyang Wang y Fulei Chu. "Hybrid machine condition monitoring based on interpretable dual tree methods using Wasserstein metrics". Expert Systems with Applications 235 (enero de 2024): 121104. http://dx.doi.org/10.1016/j.eswa.2023.121104.
Texto completoMunir, Nimra, Ross McMorrow, Konrad Mulrennan, Darren Whitaker, Seán McLoone, Minna Kellomäki, Elina Talvitie, Inari Lyyra y Marion McAfee. "Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid". Polymers 15, n.º 17 (28 de agosto de 2023): 3566. http://dx.doi.org/10.3390/polym15173566.
Texto completoQiao, Zuqiang, Shengzhi Dong, Qing Li, Xiangming Lu, Renjie Chen, Shuai Guo, Aru Yan y Wei Li. "Performance prediction models for sintered NdFeB using machine learning methods and interpretable studies". Journal of Alloys and Compounds 963 (noviembre de 2023): 171250. http://dx.doi.org/10.1016/j.jallcom.2023.171250.
Texto completoRagazzo, Michele, Stefano Melchiorri, Laura Manzo, Valeria Errichiello, Giulio Puleri, Fabio Nicastro y Emiliano Giardina. "Comparative Analysis of ANDE 6C Rapid DNA Analysis System and Traditional Methods". Genes 11, n.º 5 (22 de mayo de 2020): 582. http://dx.doi.org/10.3390/genes11050582.
Texto completoWu, Bozhi, Sen Chen, Cuiyun Gao, Lingling Fan, Yang Liu, Weiping Wen y Michael R. Lyu. "Why an Android App Is Classified as Malware". ACM Transactions on Software Engineering and Methodology 30, n.º 2 (marzo de 2021): 1–29. http://dx.doi.org/10.1145/3423096.
Texto completoXiang, Ziyu, Mingzhou Fan, Guillermo Vázquez Tovar, William Trehern, Byung-Jun Yoon, Xiaofeng Qian, Raymundo Arroyave y Xiaoning Qian. "Physics-constrained Automatic Feature Engineering for Predictive Modeling in Materials Science". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 12 (18 de mayo de 2021): 10414–21. http://dx.doi.org/10.1609/aaai.v35i12.17247.
Texto completoAbafogi, Abdo Ababor. "Survey on Interpretable Semantic Textual Similarity, and its Applications". International Journal of Innovative Technology and Exploring Engineering 10, n.º 3 (10 de enero de 2021): 14–18. http://dx.doi.org/10.35940/ijitee.b8294.0110321.
Texto completoNan, Tianlong, Yuan Gao y Christian Kroer. "Fast and Interpretable Dynamics for Fisher Markets via Block-Coordinate Updates". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 5 (26 de junio de 2023): 5832–40. http://dx.doi.org/10.1609/aaai.v37i5.25723.
Texto completoZhao, Mingyang, Junchang Xin, Zhongyang Wang, Xinlei Wang y Zhiqiong Wang. "Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation". Computational and Mathematical Methods in Medicine 2022 (31 de enero de 2022): 1–10. http://dx.doi.org/10.1155/2022/8000781.
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