Статті в журналах з теми "Model-agnostic methods"
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Su, Houcheng, Weihao Luo, Daixian Liu, Mengzhu Wang, Jing Tang, Junyang Chen, Cong Wang, and Zhenghan Chen. "Sharpness-Aware Model-Agnostic Long-Tailed Domain Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (March 24, 2024): 15091–99. http://dx.doi.org/10.1609/aaai.v38i13.29431.
Pugnana, Andrea, and Salvatore Ruggieri. "A Model-Agnostic Heuristics for Selective Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 9461–69. http://dx.doi.org/10.1609/aaai.v37i8.26133.
Satrya, Wahyu Fadli, and Ji-Hoon Yun. "Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression." Sensors 23, no. 2 (January 4, 2023): 583. http://dx.doi.org/10.3390/s23020583.
Atallah, Rasha Ragheb, Amirrudin Kamsin, Maizatul Akmar Ismail, and Ahmad Sami Al-Shamayleh. "NEURAL NETWORK WITH AGNOSTIC META-LEARNING MODEL FOR FACE-AGING RECOGNITION." Malaysian Journal of Computer Science 35, no. 1 (January 31, 2022): 56–69. http://dx.doi.org/10.22452/mjcs.vol35no1.4.
Zafar, Muhammad Rehman, and Naimul Khan. "Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability." Machine Learning and Knowledge Extraction 3, no. 3 (June 30, 2021): 525–41. http://dx.doi.org/10.3390/make3030027.
Tak, Jae-Ho, and Byung-Woo Hong. "Enhancing Model Agnostic Meta-Learning via Gradient Similarity Loss." Electronics 13, no. 3 (January 29, 2024): 535. http://dx.doi.org/10.3390/electronics13030535.
Hou, Xiaoyu, Jihui Xu, Jinming Wu, and Huaiyu Xu. "Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning." Applied Sciences 11, no. 24 (December 17, 2021): 12037. http://dx.doi.org/10.3390/app112412037.
Chen, Zhouyuan, Zhichao Lian, and Zhe Xu. "Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing." Axioms 12, no. 10 (October 23, 2023): 997. http://dx.doi.org/10.3390/axioms12100997.
Hu, Cong, Kai Xu, Zhengqiu Zhu, Long Qin, and Quanjun Yin. "Multi-Agent Chronological Planning with Model-Agnostic Meta Reinforcement Learning." Applied Sciences 13, no. 16 (August 11, 2023): 9174. http://dx.doi.org/10.3390/app13169174.
Xue, Tianfang, and Haibin Yu. "Unbiased Model-Agnostic Metalearning Algorithm for Learning Target-Driven Visual Navigation Policy." Computational Intelligence and Neuroscience 2021 (December 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/5620751.
Moskalenko, V. V. "MODEL-AGNOSTIC META-LEARNING FOR RESILIENCE OPTIMIZATION OF ARTIFICIAL INTELLIGENCE SYSTEM." Radio Electronics, Computer Science, Control, no. 2 (June 30, 2023): 79. http://dx.doi.org/10.15588/1607-3274-2023-2-9.
Schmidt, Henri, Palash Sashittal, and Benjamin J. Raphael. "A zero-agnostic model for copy number evolution in cancer." PLOS Computational Biology 19, no. 11 (November 9, 2023): e1011590. http://dx.doi.org/10.1371/journal.pcbi.1011590.
Hasan, Md Mahmudul. "Understanding Model Predictions: A Comparative Analysis of SHAP and LIME on Various ML Algorithms." Journal of Scientific and Technological Research 5, no. 1 (2024): 17–26. http://dx.doi.org/10.59738/jstr.v5i1.23(17-26).eaqr5800.
Labaien Soto, Jokin, Ekhi Zugasti Uriguen, and Xabier De Carlos Garcia. "Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders." Applied Sciences 13, no. 5 (February 24, 2023): 2912. http://dx.doi.org/10.3390/app13052912.
Sun, Yifei, Cheng Song, Feng Lu, Wei Li, Hai Jin, and Albert Y. Zomaya. "ES-Mask: Evolutionary Strip Mask for Explaining Time Series Prediction (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 16342–43. http://dx.doi.org/10.1609/aaai.v37i13.27031.
Wu, Gang, Junjun Jiang, Kui Jiang, and Xianming Liu. "Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (March 24, 2024): 5976–84. http://dx.doi.org/10.1609/aaai.v38i6.28412.
Li, Ding, Yan Liu, and Jun Huang. "Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI." Machine Learning and Knowledge Extraction 6, no. 2 (May 16, 2024): 1087–113. http://dx.doi.org/10.3390/make6020050.
Chen, Mingyang, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z. Pan, and Huajun Chen. "Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4182–90. http://dx.doi.org/10.1609/aaai.v37i4.25535.
Shozu, 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, no. 12 (December 17, 2020): 1691. http://dx.doi.org/10.3390/biom10121691.
Alinia, Parastoo, Asiful Arefeen, Zhila Esna Ashari, Seyed Iman Mirzadeh, and Hassan Ghasemzadeh. "Model-Agnostic Structural Transfer Learning for Cross-Domain Autonomous Activity Recognition." Sensors 23, no. 14 (July 12, 2023): 6337. http://dx.doi.org/10.3390/s23146337.
Apicella, A., F. Isgrò, R. Prevete, and G. Tamburrini. "Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods." International Journal of Neural Systems 30, no. 08 (July 14, 2020): 2050040. http://dx.doi.org/10.1142/s0129065720500409.
Diprose, William K., Nicholas Buist, Ning Hua, Quentin Thurier, George Shand, and Reece Robinson. "Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator." Journal of the American Medical Informatics Association 27, no. 4 (February 27, 2020): 592–600. http://dx.doi.org/10.1093/jamia/ocz229.
Liu, Jiakang, and Hua Huo. "DFENet: Double Feature Enhanced Class Agnostic Counting Methods." Frontiers in Computing and Intelligent Systems 6, no. 1 (December 1, 2023): 70–76. http://dx.doi.org/10.54097/fcis.v6i1.14.
Moon, Jae-pil, Jin-Guk Kim, Choong-Heon Yang, and 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, no. 3 (June 30, 2022): 83–95. http://dx.doi.org/10.7855/ijhe.2022.24.3.083.
Li, Jolen, Christoforos Galazis, Larion Popov, Lev Ovchinnikov, Tatyana Kharybina, Sergey Vesnin, Alexander Losev, and Igor Goryanin. "Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics." Diagnostics 12, no. 9 (August 23, 2022): 2037. http://dx.doi.org/10.3390/diagnostics12092037.
R, Jain. "Transparency in AI Decision Making: A Survey of Explainable AI Methods and Applications." Advances in Robotic Technology 2, no. 1 (January 19, 2024): 1–10. http://dx.doi.org/10.23880/art-16000110.
TOPCU, Deniz. "How to explain a machine learning model: HbA1c classification example." Journal of Medicine and Palliative Care 4, no. 2 (March 27, 2023): 117–25. http://dx.doi.org/10.47582/jompac.1259507.
Vieira, 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.
Noviandy, Teuku Rizky, Ghalieb Mutig Idroes, Irsan Hardi, Mohd Afjal, and Samrat Ray. "A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry." Infolitika Journal of Data Science 2, no. 1 (May 27, 2024): 34–44. http://dx.doi.org/10.60084/ijds.v2i1.199.
Thakur, 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 (November 2019): vi170. http://dx.doi.org/10.1093/neuonc/noz175.710.
Gunel, Kadir, and Mehmet Fatih Amasyali. "Boosting Lightweight Sentence Embeddings with Knowledge Transfer from Advanced Models: A Model-Agnostic Approach." Applied Sciences 13, no. 23 (November 22, 2023): 12586. http://dx.doi.org/10.3390/app132312586.
Kedar, Ms Mayuri Manish. "Exploring the Effectiveness of SHAP over other Explainable AI Methods." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (June 6, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem35556.
Gilo, Daniel, and Shaul Markovitch. "A General Search-Based Framework for Generating Textual Counterfactual Explanations." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (March 24, 2024): 18073–81. http://dx.doi.org/10.1609/aaai.v38i16.29764.
Song, Rui, Fausto Giunchiglia, Yingji Li, Mingjie Tian, and Hao Xu. "TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 18999–9007. http://dx.doi.org/10.1609/aaai.v38i17.29866.
Wang, Kewei, Yizheng Wu, Zhiyu Pan, Xingyi Li, Ke Xian, Zhe Wang, Zhiguo Cao, and Guosheng Lin. "Semi-supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (March 24, 2024): 5490–98. http://dx.doi.org/10.1609/aaai.v38i6.28358.
Lin, Weiping, Zhenfeng Zhuang, Lequan Yu, and 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, no. 4 (March 24, 2024): 3477–85. http://dx.doi.org/10.1609/aaai.v38i4.28135.
Ronchetti, Franco, Facundo Quiroga, Ulises Jeremias Cornejo Fandos, Gastón Gustavo Rios, Pedro Dal Bianco, Waldo Hasperué, and Laura Lanzarini. "comparison of small sample methods for Handshape Recognition." Journal of Computer Science and Technology 23, no. 1 (April 3, 2023): e03. http://dx.doi.org/10.24215/16666038.23.e03.
Demertzis, Konstantinos, and Lazaros Iliadis. "GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification." Algorithms 13, no. 3 (March 7, 2020): 61. http://dx.doi.org/10.3390/a13030061.
Fan, Xinchen, Lancheng Zou, Ziwu Liu, Yanru He, Lian Zou, and Ruan Chi. "CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning." Sensors 22, no. 10 (May 11, 2022): 3661. http://dx.doi.org/10.3390/s22103661.
Saarela, Mirka, and Lilia Geogieva. "Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model." Applied Sciences 12, no. 19 (September 23, 2022): 9545. http://dx.doi.org/10.3390/app12199545.
Winder, Isabelle, and Nick Winder. "An agnostic approach to ancient landscapes." Journal of Archaeology and Ancient History, no. 9 (February 13, 2023): 1–30. http://dx.doi.org/10.33063/jaah.vi9.130.
Akhtar, Naveed, and Mohammad Amir Asim Khan Jalwana. "Rethinking Interpretation: Input-Agnostic Saliency Mapping of Deep Visual Classifiers." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 178–86. http://dx.doi.org/10.1609/aaai.v37i1.25089.
Pruthi, Danish, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig, and 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.
Mentias, 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, no. 15 (April 11, 2023): 1121–33. http://dx.doi.org/10.1161/circulationaha.122.061995.
Yarlagadda, Sri Kalyan, Daniel Mas Montserrat, David Güera, Carol J. Boushey, Deborah A. Kerr, and Fengqing Zhu. "Saliency-Aware Class-Agnostic Food Image Segmentation." ACM Transactions on Computing for Healthcare 2, no. 3 (July 2021): 1–17. http://dx.doi.org/10.1145/3440274.
Sun, Chenyu, Hangwei Qian, and 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, no. 13 (March 24, 2024): 15145–53. http://dx.doi.org/10.1609/aaai.v38i13.29437.
Petrescu, Livia, Cătălin Petrescu, Ana Oprea, Oana Mitruț, Gabriela Moise, Alin Moldoveanu, and Florica Moldoveanu. "Machine Learning Methods for Fear Classification Based on Physiological Features." Sensors 21, no. 13 (July 1, 2021): 4519. http://dx.doi.org/10.3390/s21134519.
Rangwala, Murtaza, Jun Liu, Kulbir Singh Ahluwalia, Shayan Ghajar, Harnaik Singh Dhami, Benjamin F. Tracy, Pratap Tokekar, and Ryan K. Williams. "DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets." Agronomy 11, no. 11 (November 5, 2021): 2245. http://dx.doi.org/10.3390/agronomy11112245.
Christensen, Cade, Torrey Wagner, and Brent Langhals. "Year-Independent Prediction of Food Insecurity Using Classical and Neural Network Machine Learning Methods." AI 2, no. 2 (May 23, 2021): 244–60. http://dx.doi.org/10.3390/ai2020015.
Yamazawa, 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 (November 2019): vi224—vi225. http://dx.doi.org/10.1093/neuonc/noz175.939.