Artykuły w czasopismach na temat „Model-agnostic methods”
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Su, Houcheng, Weihao Luo, Daixian Liu, Mengzhu Wang, Jing Tang, Junyang Chen, Cong Wang i Zhenghan Chen. "Sharpness-Aware Model-Agnostic Long-Tailed Domain Generalization". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 13 (24.03.2024): 15091–99. http://dx.doi.org/10.1609/aaai.v38i13.29431.
Pełny tekst źródłaPugnana, Andrea, i Salvatore Ruggieri. "A Model-Agnostic Heuristics for Selective Classification". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 8 (26.06.2023): 9461–69. http://dx.doi.org/10.1609/aaai.v37i8.26133.
Pełny tekst źródłaSatrya, Wahyu Fadli, i Ji-Hoon Yun. "Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression". Sensors 23, nr 2 (4.01.2023): 583. http://dx.doi.org/10.3390/s23020583.
Pełny tekst źródłaAtallah, Rasha Ragheb, Amirrudin Kamsin, Maizatul Akmar Ismail i Ahmad Sami Al-Shamayleh. "NEURAL NETWORK WITH AGNOSTIC META-LEARNING MODEL FOR FACE-AGING RECOGNITION". Malaysian Journal of Computer Science 35, nr 1 (31.01.2022): 56–69. http://dx.doi.org/10.22452/mjcs.vol35no1.4.
Pełny tekst źródłaZafar, Muhammad Rehman, i Naimul Khan. "Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability". Machine Learning and Knowledge Extraction 3, nr 3 (30.06.2021): 525–41. http://dx.doi.org/10.3390/make3030027.
Pełny tekst źródłaTak, Jae-Ho, i Byung-Woo Hong. "Enhancing Model Agnostic Meta-Learning via Gradient Similarity Loss". Electronics 13, nr 3 (29.01.2024): 535. http://dx.doi.org/10.3390/electronics13030535.
Pełny tekst źródłaHou, Xiaoyu, Jihui Xu, Jinming Wu i Huaiyu Xu. "Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning". Applied Sciences 11, nr 24 (17.12.2021): 12037. http://dx.doi.org/10.3390/app112412037.
Pełny tekst źródłaChen, Zhouyuan, Zhichao Lian i Zhe Xu. "Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing". Axioms 12, nr 10 (23.10.2023): 997. http://dx.doi.org/10.3390/axioms12100997.
Pełny tekst źródłaHu, Cong, Kai Xu, Zhengqiu Zhu, Long Qin i Quanjun Yin. "Multi-Agent Chronological Planning with Model-Agnostic Meta Reinforcement Learning". Applied Sciences 13, nr 16 (11.08.2023): 9174. http://dx.doi.org/10.3390/app13169174.
Pełny tekst źródłaXue, Tianfang, i Haibin Yu. "Unbiased Model-Agnostic Metalearning Algorithm for Learning Target-Driven Visual Navigation Policy". Computational Intelligence and Neuroscience 2021 (8.12.2021): 1–12. http://dx.doi.org/10.1155/2021/5620751.
Pełny tekst źródłaMoskalenko, V. V. "MODEL-AGNOSTIC META-LEARNING FOR RESILIENCE OPTIMIZATION OF ARTIFICIAL INTELLIGENCE SYSTEM". Radio Electronics, Computer Science, Control, nr 2 (30.06.2023): 79. http://dx.doi.org/10.15588/1607-3274-2023-2-9.
Pełny tekst źródłaSchmidt, Henri, Palash Sashittal i Benjamin J. Raphael. "A zero-agnostic model for copy number evolution in cancer". PLOS Computational Biology 19, nr 11 (9.11.2023): e1011590. http://dx.doi.org/10.1371/journal.pcbi.1011590.
Pełny tekst źródłaHasan, Md Mahmudul. "Understanding Model Predictions: A Comparative Analysis of SHAP and LIME on Various ML Algorithms". Journal of Scientific and Technological Research 5, nr 1 (2024): 17–26. http://dx.doi.org/10.59738/jstr.v5i1.23(17-26).eaqr5800.
Pełny tekst źródłaLabaien Soto, Jokin, Ekhi Zugasti Uriguen i Xabier De Carlos Garcia. "Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders". Applied Sciences 13, nr 5 (24.02.2023): 2912. http://dx.doi.org/10.3390/app13052912.
Pełny tekst źródłaSun, Yifei, Cheng Song, Feng Lu, Wei Li, Hai Jin i Albert Y. Zomaya. "ES-Mask: Evolutionary Strip Mask for Explaining Time Series Prediction (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 13 (26.06.2023): 16342–43. http://dx.doi.org/10.1609/aaai.v37i13.27031.
Pełny tekst źródłaWu, Gang, Junjun Jiang, Kui Jiang i Xianming Liu. "Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 6 (24.03.2024): 5976–84. http://dx.doi.org/10.1609/aaai.v38i6.28412.
Pełny tekst źródłaLi, Ding, Yan Liu i Jun Huang. "Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI". Machine Learning and Knowledge Extraction 6, nr 2 (16.05.2024): 1087–113. http://dx.doi.org/10.3390/make6020050.
Pełny tekst źródłaChen, Mingyang, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z. Pan i Huajun Chen. "Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 4 (26.06.2023): 4182–90. http://dx.doi.org/10.1609/aaai.v37i4.25535.
Pełny tekst źródłaShozu, Kanto, Masaaki Komatsu, Akira Sakai, Reina Komatsu, Ai Dozen, Hidenori Machino, Suguru Yasutomi i in. "Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos". Biomolecules 10, nr 12 (17.12.2020): 1691. http://dx.doi.org/10.3390/biom10121691.
Pełny tekst źródłaAlinia, Parastoo, Asiful Arefeen, Zhila Esna Ashari, Seyed Iman Mirzadeh i Hassan Ghasemzadeh. "Model-Agnostic Structural Transfer Learning for Cross-Domain Autonomous Activity Recognition". Sensors 23, nr 14 (12.07.2023): 6337. http://dx.doi.org/10.3390/s23146337.
Pełny tekst źródłaApicella, A., F. Isgrò, R. Prevete i G. Tamburrini. "Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods". International Journal of Neural Systems 30, nr 08 (14.07.2020): 2050040. http://dx.doi.org/10.1142/s0129065720500409.
Pełny tekst źródłaDiprose, William K., Nicholas Buist, Ning Hua, Quentin Thurier, George Shand i Reece Robinson. "Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator". Journal of the American Medical Informatics Association 27, nr 4 (27.02.2020): 592–600. http://dx.doi.org/10.1093/jamia/ocz229.
Pełny tekst źródłaLiu, Jiakang, i Hua Huo. "DFENet: Double Feature Enhanced Class Agnostic Counting Methods". Frontiers in Computing and Intelligent Systems 6, nr 1 (1.12.2023): 70–76. http://dx.doi.org/10.54097/fcis.v6i1.14.
Pełny tekst źródłaMoon, Jae-pil, Jin-Guk Kim, Choong-Heon Yang i 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, nr 3 (30.06.2022): 83–95. http://dx.doi.org/10.7855/ijhe.2022.24.3.083.
Pełny tekst źródłaLi, Jolen, Christoforos Galazis, Larion Popov, Lev Ovchinnikov, Tatyana Kharybina, Sergey Vesnin, Alexander Losev i Igor Goryanin. "Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics". Diagnostics 12, nr 9 (23.08.2022): 2037. http://dx.doi.org/10.3390/diagnostics12092037.
Pełny tekst źródłaR, Jain. "Transparency in AI Decision Making: A Survey of Explainable AI Methods and Applications". Advances in Robotic Technology 2, nr 1 (19.01.2024): 1–10. http://dx.doi.org/10.23880/art-16000110.
Pełny tekst źródłaTOPCU, Deniz. "How to explain a machine learning model: HbA1c classification example". Journal of Medicine and Palliative Care 4, nr 2 (27.03.2023): 117–25. http://dx.doi.org/10.47582/jompac.1259507.
Pełny tekst źródłaVieira, Carla Piazzon Ramos, i Luciano Antonio Digiampietri. "A study about Explainable Articial Intelligence: using decision tree to explain SVM". Revista Brasileira de Computação Aplicada 12, nr 1 (8.01.2020): 113–21. http://dx.doi.org/10.5335/rbca.v12i1.10247.
Pełny tekst źródłaNoviandy, Teuku Rizky, Ghalieb Mutig Idroes, Irsan Hardi, Mohd Afjal i Samrat Ray. "A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry". Infolitika Journal of Data Science 2, nr 1 (27.05.2024): 34–44. http://dx.doi.org/10.60084/ijds.v2i1.199.
Pełny tekst źródłaThakur, Siddhesh, Jimit Doshi, Sung Min Ha, Gaurav Shukla, Aikaterini Kotrotsou, Sanjay Talbar, Uday Kulkarni i in. "NIMG-40. ROBUST MODALITY-AGNOSTIC SKULL-STRIPPING IN PRESENCE OF DIFFUSE GLIOMA: A MULTI-INSTITUTIONAL STUDY". Neuro-Oncology 21, Supplement_6 (listopad 2019): vi170. http://dx.doi.org/10.1093/neuonc/noz175.710.
Pełny tekst źródłaGunel, Kadir, i Mehmet Fatih Amasyali. "Boosting Lightweight Sentence Embeddings with Knowledge Transfer from Advanced Models: A Model-Agnostic Approach". Applied Sciences 13, nr 23 (22.11.2023): 12586. http://dx.doi.org/10.3390/app132312586.
Pełny tekst źródłaKedar, Ms Mayuri Manish. "Exploring the Effectiveness of SHAP over other Explainable AI Methods". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, nr 06 (6.06.2024): 1–5. http://dx.doi.org/10.55041/ijsrem35556.
Pełny tekst źródłaGilo, Daniel, i Shaul Markovitch. "A General Search-Based Framework for Generating Textual Counterfactual Explanations". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 16 (24.03.2024): 18073–81. http://dx.doi.org/10.1609/aaai.v38i16.29764.
Pełny tekst źródłaSong, Rui, Fausto Giunchiglia, Yingji Li, Mingjie Tian i Hao Xu. "TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 17 (24.03.2024): 18999–9007. http://dx.doi.org/10.1609/aaai.v38i17.29866.
Pełny tekst źródłaWang, Kewei, Yizheng Wu, Zhiyu Pan, Xingyi Li, Ke Xian, Zhe Wang, Zhiguo Cao i Guosheng Lin. "Semi-supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 6 (24.03.2024): 5490–98. http://dx.doi.org/10.1609/aaai.v38i6.28358.
Pełny tekst źródłaLin, Weiping, Zhenfeng Zhuang, Lequan Yu i 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, nr 4 (24.03.2024): 3477–85. http://dx.doi.org/10.1609/aaai.v38i4.28135.
Pełny tekst źródłaRonchetti, Franco, Facundo Quiroga, Ulises Jeremias Cornejo Fandos, Gastón Gustavo Rios, Pedro Dal Bianco, Waldo Hasperué i Laura Lanzarini. "comparison of small sample methods for Handshape Recognition". Journal of Computer Science and Technology 23, nr 1 (3.04.2023): e03. http://dx.doi.org/10.24215/16666038.23.e03.
Pełny tekst źródłaDemertzis, Konstantinos, i Lazaros Iliadis. "GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification". Algorithms 13, nr 3 (7.03.2020): 61. http://dx.doi.org/10.3390/a13030061.
Pełny tekst źródłaFan, Xinchen, Lancheng Zou, Ziwu Liu, Yanru He, Lian Zou i Ruan Chi. "CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning". Sensors 22, nr 10 (11.05.2022): 3661. http://dx.doi.org/10.3390/s22103661.
Pełny tekst źródłaSaarela, Mirka, i Lilia Geogieva. "Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model". Applied Sciences 12, nr 19 (23.09.2022): 9545. http://dx.doi.org/10.3390/app12199545.
Pełny tekst źródłaWinder, Isabelle, i Nick Winder. "An agnostic approach to ancient landscapes". Journal of Archaeology and Ancient History, nr 9 (13.02.2023): 1–30. http://dx.doi.org/10.33063/jaah.vi9.130.
Pełny tekst źródłaAkhtar, Naveed, i Mohammad Amir Asim Khan Jalwana. "Rethinking Interpretation: Input-Agnostic Saliency Mapping of Deep Visual Classifiers". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 1 (26.06.2023): 178–86. http://dx.doi.org/10.1609/aaai.v37i1.25089.
Pełny tekst źródłaPruthi, Danish, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig i 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.
Pełny tekst źródłaMentias, Amgad, Eric D. Peterson, Neil Keshvani, Dharam J. Kumbhani, Clyde W. Yancy, Alanna A. Morris, Larry A. Allen i in. "Achieving Equity in Hospital Performance Assessments Using Composite Race-Specific Measures of Risk-Standardized Readmission and Mortality Rates for Heart Failure". Circulation 147, nr 15 (11.04.2023): 1121–33. http://dx.doi.org/10.1161/circulationaha.122.061995.
Pełny tekst źródłaYarlagadda, Sri Kalyan, Daniel Mas Montserrat, David Güera, Carol J. Boushey, Deborah A. Kerr i Fengqing Zhu. "Saliency-Aware Class-Agnostic Food Image Segmentation". ACM Transactions on Computing for Healthcare 2, nr 3 (lipiec 2021): 1–17. http://dx.doi.org/10.1145/3440274.
Pełny tekst źródłaSun, Chenyu, Hangwei Qian i 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, nr 13 (24.03.2024): 15145–53. http://dx.doi.org/10.1609/aaai.v38i13.29437.
Pełny tekst źródłaPetrescu, Livia, Cătălin Petrescu, Ana Oprea, Oana Mitruț, Gabriela Moise, Alin Moldoveanu i Florica Moldoveanu. "Machine Learning Methods for Fear Classification Based on Physiological Features". Sensors 21, nr 13 (1.07.2021): 4519. http://dx.doi.org/10.3390/s21134519.
Pełny tekst źródłaRangwala, Murtaza, Jun Liu, Kulbir Singh Ahluwalia, Shayan Ghajar, Harnaik Singh Dhami, Benjamin F. Tracy, Pratap Tokekar i Ryan K. Williams. "DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets". Agronomy 11, nr 11 (5.11.2021): 2245. http://dx.doi.org/10.3390/agronomy11112245.
Pełny tekst źródłaChristensen, Cade, Torrey Wagner i Brent Langhals. "Year-Independent Prediction of Food Insecurity Using Classical and Neural Network Machine Learning Methods". AI 2, nr 2 (23.05.2021): 244–60. http://dx.doi.org/10.3390/ai2020015.
Pełny tekst źródłaYamazawa, Erika, Satoshi Takahashi, Shota Tanaka, Wataru Takahashi, Takahiro Nakamoto, Shunsaku Takayanagi, Yosuke Kitagawa i in. "RARE-16. A NOVEL RADIOMICS MODEL DIFFERENTIATING CHORDOMA AND CHONDROSARCOMA". Neuro-Oncology 21, Supplement_6 (listopad 2019): vi224—vi225. http://dx.doi.org/10.1093/neuonc/noz175.939.
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