Artículos de revistas sobre el tema "Model-agnostic methods"
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Su, Houcheng, Weihao Luo, Daixian Liu, Mengzhu Wang, Jing Tang, Junyang Chen, Cong Wang y Zhenghan Chen. "Sharpness-Aware Model-Agnostic Long-Tailed Domain Generalization". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 13 (24 de marzo de 2024): 15091–99. http://dx.doi.org/10.1609/aaai.v38i13.29431.
Texto completoPugnana, Andrea y Salvatore Ruggieri. "A Model-Agnostic Heuristics for Selective Classification". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 8 (26 de junio de 2023): 9461–69. http://dx.doi.org/10.1609/aaai.v37i8.26133.
Texto completoSatrya, Wahyu Fadli y Ji-Hoon Yun. "Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression". Sensors 23, n.º 2 (4 de enero de 2023): 583. http://dx.doi.org/10.3390/s23020583.
Texto completoAtallah, Rasha Ragheb, Amirrudin Kamsin, Maizatul Akmar Ismail y Ahmad Sami Al-Shamayleh. "NEURAL NETWORK WITH AGNOSTIC META-LEARNING MODEL FOR FACE-AGING RECOGNITION". Malaysian Journal of Computer Science 35, n.º 1 (31 de enero de 2022): 56–69. http://dx.doi.org/10.22452/mjcs.vol35no1.4.
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 completoTak, Jae-Ho y Byung-Woo Hong. "Enhancing Model Agnostic Meta-Learning via Gradient Similarity Loss". Electronics 13, n.º 3 (29 de enero de 2024): 535. http://dx.doi.org/10.3390/electronics13030535.
Texto completoHou, Xiaoyu, Jihui Xu, Jinming Wu y Huaiyu Xu. "Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning". Applied Sciences 11, n.º 24 (17 de diciembre de 2021): 12037. http://dx.doi.org/10.3390/app112412037.
Texto completoChen, Zhouyuan, Zhichao Lian y Zhe Xu. "Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing". Axioms 12, n.º 10 (23 de octubre de 2023): 997. http://dx.doi.org/10.3390/axioms12100997.
Texto completoHu, Cong, Kai Xu, Zhengqiu Zhu, Long Qin y Quanjun Yin. "Multi-Agent Chronological Planning with Model-Agnostic Meta Reinforcement Learning". Applied Sciences 13, n.º 16 (11 de agosto de 2023): 9174. http://dx.doi.org/10.3390/app13169174.
Texto completoXue, Tianfang y Haibin Yu. "Unbiased Model-Agnostic Metalearning Algorithm for Learning Target-Driven Visual Navigation Policy". Computational Intelligence and Neuroscience 2021 (8 de diciembre de 2021): 1–12. http://dx.doi.org/10.1155/2021/5620751.
Texto completoMoskalenko, V. V. "MODEL-AGNOSTIC META-LEARNING FOR RESILIENCE OPTIMIZATION OF ARTIFICIAL INTELLIGENCE SYSTEM". Radio Electronics, Computer Science, Control, n.º 2 (30 de junio de 2023): 79. http://dx.doi.org/10.15588/1607-3274-2023-2-9.
Texto completoSchmidt, Henri, Palash Sashittal y Benjamin J. Raphael. "A zero-agnostic model for copy number evolution in cancer". PLOS Computational Biology 19, n.º 11 (9 de noviembre de 2023): e1011590. http://dx.doi.org/10.1371/journal.pcbi.1011590.
Texto completoHasan, Md Mahmudul. "Understanding Model Predictions: A Comparative Analysis of SHAP and LIME on Various ML Algorithms". Journal of Scientific and Technological Research 5, n.º 1 (2024): 17–26. http://dx.doi.org/10.59738/jstr.v5i1.23(17-26).eaqr5800.
Texto completoLabaien Soto, Jokin, Ekhi Zugasti Uriguen y Xabier De Carlos Garcia. "Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders". Applied Sciences 13, n.º 5 (24 de febrero de 2023): 2912. http://dx.doi.org/10.3390/app13052912.
Texto completoSun, Yifei, Cheng Song, Feng Lu, Wei Li, Hai Jin y Albert Y. Zomaya. "ES-Mask: Evolutionary Strip Mask for Explaining Time Series Prediction (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junio de 2023): 16342–43. http://dx.doi.org/10.1609/aaai.v37i13.27031.
Texto completoWu, Gang, Junjun Jiang, Kui Jiang y Xianming Liu. "Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 6 (24 de marzo de 2024): 5976–84. http://dx.doi.org/10.1609/aaai.v38i6.28412.
Texto completoLi, Ding, Yan Liu y Jun Huang. "Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI". Machine Learning and Knowledge Extraction 6, n.º 2 (16 de mayo de 2024): 1087–113. http://dx.doi.org/10.3390/make6020050.
Texto completoChen, Mingyang, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z. Pan y Huajun Chen. "Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 4 (26 de junio de 2023): 4182–90. http://dx.doi.org/10.1609/aaai.v37i4.25535.
Texto completoShozu, Kanto, Masaaki Komatsu, Akira Sakai, Reina Komatsu, Ai Dozen, Hidenori Machino, Suguru Yasutomi et al. "Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos". Biomolecules 10, n.º 12 (17 de diciembre de 2020): 1691. http://dx.doi.org/10.3390/biom10121691.
Texto completoAlinia, Parastoo, Asiful Arefeen, Zhila Esna Ashari, Seyed Iman Mirzadeh y Hassan Ghasemzadeh. "Model-Agnostic Structural Transfer Learning for Cross-Domain Autonomous Activity Recognition". Sensors 23, n.º 14 (12 de julio de 2023): 6337. http://dx.doi.org/10.3390/s23146337.
Texto completoApicella, A., F. Isgrò, R. Prevete y G. Tamburrini. "Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods". International Journal of Neural Systems 30, n.º 08 (14 de julio de 2020): 2050040. http://dx.doi.org/10.1142/s0129065720500409.
Texto completoDiprose, 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 completoLiu, Jiakang y Hua Huo. "DFENet: Double Feature Enhanced Class Agnostic Counting Methods". Frontiers in Computing and Intelligent Systems 6, n.º 1 (1 de diciembre de 2023): 70–76. http://dx.doi.org/10.54097/fcis.v6i1.14.
Texto completoMoon, Jae-pil, Jin-Guk Kim, Choong-Heon Yang y Su-Bin Park. "A Case Study on the Application of Model-agnostic Methods for the Post hoc Interpretation of A Machine Learning Model :". International Journal of Highway Engineering 24, n.º 3 (30 de junio de 2022): 83–95. http://dx.doi.org/10.7855/ijhe.2022.24.3.083.
Texto completoLi, Jolen, Christoforos Galazis, Larion Popov, Lev Ovchinnikov, Tatyana Kharybina, Sergey Vesnin, Alexander Losev y Igor Goryanin. "Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics". Diagnostics 12, n.º 9 (23 de agosto de 2022): 2037. http://dx.doi.org/10.3390/diagnostics12092037.
Texto completoR, Jain. "Transparency in AI Decision Making: A Survey of Explainable AI Methods and Applications". Advances in Robotic Technology 2, n.º 1 (19 de enero de 2024): 1–10. http://dx.doi.org/10.23880/art-16000110.
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 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 completoNoviandy, Teuku Rizky, Ghalieb Mutig Idroes, Irsan Hardi, Mohd Afjal y Samrat Ray. "A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry". Infolitika Journal of Data Science 2, n.º 1 (27 de mayo de 2024): 34–44. http://dx.doi.org/10.60084/ijds.v2i1.199.
Texto completoThakur, Siddhesh, Jimit Doshi, Sung Min Ha, Gaurav Shukla, Aikaterini Kotrotsou, Sanjay Talbar, Uday Kulkarni et al. "NIMG-40. ROBUST MODALITY-AGNOSTIC SKULL-STRIPPING IN PRESENCE OF DIFFUSE GLIOMA: A MULTI-INSTITUTIONAL STUDY". Neuro-Oncology 21, Supplement_6 (noviembre de 2019): vi170. http://dx.doi.org/10.1093/neuonc/noz175.710.
Texto completoGunel, Kadir y Mehmet Fatih Amasyali. "Boosting Lightweight Sentence Embeddings with Knowledge Transfer from Advanced Models: A Model-Agnostic Approach". Applied Sciences 13, n.º 23 (22 de noviembre de 2023): 12586. http://dx.doi.org/10.3390/app132312586.
Texto completoKedar, Ms Mayuri Manish. "Exploring the Effectiveness of SHAP over other Explainable AI Methods". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 06 (6 de junio de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem35556.
Texto completoGilo, Daniel y Shaul Markovitch. "A General Search-Based Framework for Generating Textual Counterfactual Explanations". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 16 (24 de marzo de 2024): 18073–81. http://dx.doi.org/10.1609/aaai.v38i16.29764.
Texto completoSong, Rui, Fausto Giunchiglia, Yingji Li, Mingjie Tian y Hao Xu. "TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 17 (24 de marzo de 2024): 18999–9007. http://dx.doi.org/10.1609/aaai.v38i17.29866.
Texto completoWang, Kewei, Yizheng Wu, Zhiyu Pan, Xingyi Li, Ke Xian, Zhe Wang, Zhiguo Cao y Guosheng Lin. "Semi-supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 6 (24 de marzo de 2024): 5490–98. http://dx.doi.org/10.1609/aaai.v38i6.28358.
Texto completoLin, Weiping, Zhenfeng Zhuang, Lequan Yu y Liansheng Wang. "Boosting Multiple Instance Learning Models for Whole Slide Image Classification: A Model-Agnostic Framework Based on Counterfactual Inference". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 4 (24 de marzo de 2024): 3477–85. http://dx.doi.org/10.1609/aaai.v38i4.28135.
Texto completoRonchetti, Franco, Facundo Quiroga, Ulises Jeremias Cornejo Fandos, Gastón Gustavo Rios, Pedro Dal Bianco, Waldo Hasperué y Laura Lanzarini. "comparison of small sample methods for Handshape Recognition". Journal of Computer Science and Technology 23, n.º 1 (3 de abril de 2023): e03. http://dx.doi.org/10.24215/16666038.23.e03.
Texto completoDemertzis, Konstantinos y Lazaros Iliadis. "GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification". Algorithms 13, n.º 3 (7 de marzo de 2020): 61. http://dx.doi.org/10.3390/a13030061.
Texto completoFan, Xinchen, Lancheng Zou, Ziwu Liu, Yanru He, Lian Zou y Ruan Chi. "CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning". Sensors 22, n.º 10 (11 de mayo de 2022): 3661. http://dx.doi.org/10.3390/s22103661.
Texto completoSaarela, Mirka y Lilia Geogieva. "Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model". Applied Sciences 12, n.º 19 (23 de septiembre de 2022): 9545. http://dx.doi.org/10.3390/app12199545.
Texto completoWinder, Isabelle y Nick Winder. "An agnostic approach to ancient landscapes". Journal of Archaeology and Ancient History, n.º 9 (13 de febrero de 2023): 1–30. http://dx.doi.org/10.33063/jaah.vi9.130.
Texto completoAkhtar, Naveed y Mohammad Amir Asim Khan Jalwana. "Rethinking Interpretation: Input-Agnostic Saliency Mapping of Deep Visual Classifiers". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 1 (26 de junio de 2023): 178–86. http://dx.doi.org/10.1609/aaai.v37i1.25089.
Texto completoPruthi, Danish, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig y William W. Cohen. "Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?" Transactions of the Association for Computational Linguistics 10 (2022): 359–75. http://dx.doi.org/10.1162/tacl_a_00465.
Texto completoMentias, Amgad, Eric D. Peterson, Neil Keshvani, Dharam J. Kumbhani, Clyde W. Yancy, Alanna A. Morris, Larry A. Allen et al. "Achieving Equity in Hospital Performance Assessments Using Composite Race-Specific Measures of Risk-Standardized Readmission and Mortality Rates for Heart Failure". Circulation 147, n.º 15 (11 de abril de 2023): 1121–33. http://dx.doi.org/10.1161/circulationaha.122.061995.
Texto completoYarlagadda, Sri Kalyan, Daniel Mas Montserrat, David Güera, Carol J. Boushey, Deborah A. Kerr y Fengqing Zhu. "Saliency-Aware Class-Agnostic Food Image Segmentation". ACM Transactions on Computing for Healthcare 2, n.º 3 (julio de 2021): 1–17. http://dx.doi.org/10.1145/3440274.
Texto completoSun, Chenyu, Hangwei Qian y Chunyan Miao. "CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 13 (24 de marzo de 2024): 15145–53. http://dx.doi.org/10.1609/aaai.v38i13.29437.
Texto completoPetrescu, Livia, Cătălin Petrescu, Ana Oprea, Oana Mitruț, Gabriela Moise, Alin Moldoveanu y Florica Moldoveanu. "Machine Learning Methods for Fear Classification Based on Physiological Features". Sensors 21, n.º 13 (1 de julio de 2021): 4519. http://dx.doi.org/10.3390/s21134519.
Texto completoRangwala, Murtaza, Jun Liu, Kulbir Singh Ahluwalia, Shayan Ghajar, Harnaik Singh Dhami, Benjamin F. Tracy, Pratap Tokekar y Ryan K. Williams. "DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets". Agronomy 11, n.º 11 (5 de noviembre de 2021): 2245. http://dx.doi.org/10.3390/agronomy11112245.
Texto completoChristensen, Cade, Torrey Wagner y Brent Langhals. "Year-Independent Prediction of Food Insecurity Using Classical and Neural Network Machine Learning Methods". AI 2, n.º 2 (23 de mayo de 2021): 244–60. http://dx.doi.org/10.3390/ai2020015.
Texto completoYamazawa, Erika, Satoshi Takahashi, Shota Tanaka, Wataru Takahashi, Takahiro Nakamoto, Shunsaku Takayanagi, Yosuke Kitagawa et al. "RARE-16. A NOVEL RADIOMICS MODEL DIFFERENTIATING CHORDOMA AND CHONDROSARCOMA". Neuro-Oncology 21, Supplement_6 (noviembre de 2019): vi224—vi225. http://dx.doi.org/10.1093/neuonc/noz175.939.
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