Artigos de revistas sobre o tema "Model-agnostic methods"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Veja os 50 melhores artigos de revistas para estudos sobre o assunto "Model-agnostic methods".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Veja os artigos de revistas das mais diversas áreas científicas e compile uma bibliografia correta.
Su, Houcheng, Weihao Luo, Daixian Liu, Mengzhu Wang, Jing Tang, Junyang Chen, Cong Wang e Zhenghan Chen. "Sharpness-Aware Model-Agnostic Long-Tailed Domain Generalization". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 13 (24 de março de 2024): 15091–99. http://dx.doi.org/10.1609/aaai.v38i13.29431.
Texto completo da fontePugnana, Andrea, e Salvatore Ruggieri. "A Model-Agnostic Heuristics for Selective Classification". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 8 (26 de junho de 2023): 9461–69. http://dx.doi.org/10.1609/aaai.v37i8.26133.
Texto completo da fonteSatrya, Wahyu Fadli, e Ji-Hoon Yun. "Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression". Sensors 23, n.º 2 (4 de janeiro de 2023): 583. http://dx.doi.org/10.3390/s23020583.
Texto completo da fonteAtallah, Rasha Ragheb, Amirrudin Kamsin, Maizatul Akmar Ismail e 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 janeiro de 2022): 56–69. http://dx.doi.org/10.22452/mjcs.vol35no1.4.
Texto completo da fonteZafar, Muhammad Rehman, e Naimul Khan. "Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability". Machine Learning and Knowledge Extraction 3, n.º 3 (30 de junho de 2021): 525–41. http://dx.doi.org/10.3390/make3030027.
Texto completo da fonteTak, Jae-Ho, e Byung-Woo Hong. "Enhancing Model Agnostic Meta-Learning via Gradient Similarity Loss". Electronics 13, n.º 3 (29 de janeiro de 2024): 535. http://dx.doi.org/10.3390/electronics13030535.
Texto completo da fonteHou, Xiaoyu, Jihui Xu, Jinming Wu e Huaiyu Xu. "Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning". Applied Sciences 11, n.º 24 (17 de dezembro de 2021): 12037. http://dx.doi.org/10.3390/app112412037.
Texto completo da fonteChen, Zhouyuan, Zhichao Lian e Zhe Xu. "Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing". Axioms 12, n.º 10 (23 de outubro de 2023): 997. http://dx.doi.org/10.3390/axioms12100997.
Texto completo da fonteHu, Cong, Kai Xu, Zhengqiu Zhu, Long Qin e 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 completo da fonteXue, Tianfang, e Haibin Yu. "Unbiased Model-Agnostic Metalearning Algorithm for Learning Target-Driven Visual Navigation Policy". Computational Intelligence and Neuroscience 2021 (8 de dezembro de 2021): 1–12. http://dx.doi.org/10.1155/2021/5620751.
Texto completo da fonteMoskalenko, V. V. "MODEL-AGNOSTIC META-LEARNING FOR RESILIENCE OPTIMIZATION OF ARTIFICIAL INTELLIGENCE SYSTEM". Radio Electronics, Computer Science, Control, n.º 2 (30 de junho de 2023): 79. http://dx.doi.org/10.15588/1607-3274-2023-2-9.
Texto completo da fonteSchmidt, Henri, Palash Sashittal e Benjamin J. Raphael. "A zero-agnostic model for copy number evolution in cancer". PLOS Computational Biology 19, n.º 11 (9 de novembro de 2023): e1011590. http://dx.doi.org/10.1371/journal.pcbi.1011590.
Texto completo da fonteHasan, 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 completo da fonteLabaien Soto, Jokin, Ekhi Zugasti Uriguen e Xabier De Carlos Garcia. "Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders". Applied Sciences 13, n.º 5 (24 de fevereiro de 2023): 2912. http://dx.doi.org/10.3390/app13052912.
Texto completo da fonteSun, Yifei, Cheng Song, Feng Lu, Wei Li, Hai Jin e 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 junho de 2023): 16342–43. http://dx.doi.org/10.1609/aaai.v37i13.27031.
Texto completo da fonteWu, Gang, Junjun Jiang, Kui Jiang e 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 março de 2024): 5976–84. http://dx.doi.org/10.1609/aaai.v38i6.28412.
Texto completo da fonteLi, Ding, Yan Liu e Jun Huang. "Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI". Machine Learning and Knowledge Extraction 6, n.º 2 (16 de maio de 2024): 1087–113. http://dx.doi.org/10.3390/make6020050.
Texto completo da fonteChen, Mingyang, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z. Pan e 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 junho de 2023): 4182–90. http://dx.doi.org/10.1609/aaai.v37i4.25535.
Texto completo da fonteShozu, 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 dezembro de 2020): 1691. http://dx.doi.org/10.3390/biom10121691.
Texto completo da fonteAlinia, Parastoo, Asiful Arefeen, Zhila Esna Ashari, Seyed Iman Mirzadeh e Hassan Ghasemzadeh. "Model-Agnostic Structural Transfer Learning for Cross-Domain Autonomous Activity Recognition". Sensors 23, n.º 14 (12 de julho de 2023): 6337. http://dx.doi.org/10.3390/s23146337.
Texto completo da fonteApicella, A., F. Isgrò, R. Prevete e 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 julho de 2020): 2050040. http://dx.doi.org/10.1142/s0129065720500409.
Texto completo da fonteDiprose, William K., Nicholas Buist, Ning Hua, Quentin Thurier, George Shand e 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 fevereiro de 2020): 592–600. http://dx.doi.org/10.1093/jamia/ocz229.
Texto completo da fonteLiu, Jiakang, e Hua Huo. "DFENet: Double Feature Enhanced Class Agnostic Counting Methods". Frontiers in Computing and Intelligent Systems 6, n.º 1 (1 de dezembro de 2023): 70–76. http://dx.doi.org/10.54097/fcis.v6i1.14.
Texto completo da fonteMoon, Jae-pil, Jin-Guk Kim, Choong-Heon Yang e 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 junho de 2022): 83–95. http://dx.doi.org/10.7855/ijhe.2022.24.3.083.
Texto completo da fonteLi, Jolen, Christoforos Galazis, Larion Popov, Lev Ovchinnikov, Tatyana Kharybina, Sergey Vesnin, Alexander Losev e 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 completo da fonteR, Jain. "Transparency in AI Decision Making: A Survey of Explainable AI Methods and Applications". Advances in Robotic Technology 2, n.º 1 (19 de janeiro de 2024): 1–10. http://dx.doi.org/10.23880/art-16000110.
Texto completo da fonteTOPCU, Deniz. "How to explain a machine learning model: HbA1c classification example". Journal of Medicine and Palliative Care 4, n.º 2 (27 de março de 2023): 117–25. http://dx.doi.org/10.47582/jompac.1259507.
Texto completo da fonteVieira, Carla Piazzon Ramos, e 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 janeiro de 2020): 113–21. http://dx.doi.org/10.5335/rbca.v12i1.10247.
Texto completo da fonteNoviandy, Teuku Rizky, Ghalieb Mutig Idroes, Irsan Hardi, Mohd Afjal e 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 maio de 2024): 34–44. http://dx.doi.org/10.60084/ijds.v2i1.199.
Texto completo da fonteThakur, 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 (novembro de 2019): vi170. http://dx.doi.org/10.1093/neuonc/noz175.710.
Texto completo da fonteGunel, Kadir, e 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 novembro de 2023): 12586. http://dx.doi.org/10.3390/app132312586.
Texto completo da fonteKedar, 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 junho de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem35556.
Texto completo da fonteGilo, Daniel, e 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 março de 2024): 18073–81. http://dx.doi.org/10.1609/aaai.v38i16.29764.
Texto completo da fonteSong, Rui, Fausto Giunchiglia, Yingji Li, Mingjie Tian e 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 março de 2024): 18999–9007. http://dx.doi.org/10.1609/aaai.v38i17.29866.
Texto completo da fonteWang, Kewei, Yizheng Wu, Zhiyu Pan, Xingyi Li, Ke Xian, Zhe Wang, Zhiguo Cao e 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 março de 2024): 5490–98. http://dx.doi.org/10.1609/aaai.v38i6.28358.
Texto completo da fonteLin, Weiping, Zhenfeng Zhuang, Lequan Yu e 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 março de 2024): 3477–85. http://dx.doi.org/10.1609/aaai.v38i4.28135.
Texto completo da fonteRonchetti, Franco, Facundo Quiroga, Ulises Jeremias Cornejo Fandos, Gastón Gustavo Rios, Pedro Dal Bianco, Waldo Hasperué e 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 completo da fonteDemertzis, Konstantinos, e 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 março de 2020): 61. http://dx.doi.org/10.3390/a13030061.
Texto completo da fonteFan, Xinchen, Lancheng Zou, Ziwu Liu, Yanru He, Lian Zou e Ruan Chi. "CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning". Sensors 22, n.º 10 (11 de maio de 2022): 3661. http://dx.doi.org/10.3390/s22103661.
Texto completo da fonteSaarela, Mirka, e Lilia Geogieva. "Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model". Applied Sciences 12, n.º 19 (23 de setembro de 2022): 9545. http://dx.doi.org/10.3390/app12199545.
Texto completo da fonteWinder, Isabelle, e Nick Winder. "An agnostic approach to ancient landscapes". Journal of Archaeology and Ancient History, n.º 9 (13 de fevereiro de 2023): 1–30. http://dx.doi.org/10.33063/jaah.vi9.130.
Texto completo da fonteAkhtar, Naveed, e 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 junho de 2023): 178–86. http://dx.doi.org/10.1609/aaai.v37i1.25089.
Texto completo da fontePruthi, Danish, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig e 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 completo da fonteMentias, 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 completo da fonteYarlagadda, Sri Kalyan, Daniel Mas Montserrat, David Güera, Carol J. Boushey, Deborah A. Kerr e Fengqing Zhu. "Saliency-Aware Class-Agnostic Food Image Segmentation". ACM Transactions on Computing for Healthcare 2, n.º 3 (julho de 2021): 1–17. http://dx.doi.org/10.1145/3440274.
Texto completo da fonteSun, Chenyu, Hangwei Qian e 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 março de 2024): 15145–53. http://dx.doi.org/10.1609/aaai.v38i13.29437.
Texto completo da fontePetrescu, Livia, Cătălin Petrescu, Ana Oprea, Oana Mitruț, Gabriela Moise, Alin Moldoveanu e Florica Moldoveanu. "Machine Learning Methods for Fear Classification Based on Physiological Features". Sensors 21, n.º 13 (1 de julho de 2021): 4519. http://dx.doi.org/10.3390/s21134519.
Texto completo da fonteRangwala, Murtaza, Jun Liu, Kulbir Singh Ahluwalia, Shayan Ghajar, Harnaik Singh Dhami, Benjamin F. Tracy, Pratap Tokekar e 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 novembro de 2021): 2245. http://dx.doi.org/10.3390/agronomy11112245.
Texto completo da fonteChristensen, Cade, Torrey Wagner e Brent Langhals. "Year-Independent Prediction of Food Insecurity Using Classical and Neural Network Machine Learning Methods". AI 2, n.º 2 (23 de maio de 2021): 244–60. http://dx.doi.org/10.3390/ai2020015.
Texto completo da fonteYamazawa, 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 (novembro de 2019): vi224—vi225. http://dx.doi.org/10.1093/neuonc/noz175.939.
Texto completo da fonte