Zeitschriftenartikel zum Thema „Model-agnostic methods“
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Su, Houcheng, Weihao Luo, Daixian Liu, Mengzhu Wang, Jing Tang, Junyang Chen, Cong Wang und 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.
Der volle Inhalt der QuellePugnana, Andrea, und 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.
Der volle Inhalt der QuelleSatrya, Wahyu Fadli, und Ji-Hoon Yun. „Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression“. Sensors 23, Nr. 2 (04.01.2023): 583. http://dx.doi.org/10.3390/s23020583.
Der volle Inhalt der QuelleAtallah, Rasha Ragheb, Amirrudin Kamsin, Maizatul Akmar Ismail und 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.
Der volle Inhalt der QuelleZafar, Muhammad Rehman, und 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.
Der volle Inhalt der QuelleTak, Jae-Ho, und 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.
Der volle Inhalt der QuelleHou, Xiaoyu, Jihui Xu, Jinming Wu und 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.
Der volle Inhalt der QuelleChen, Zhouyuan, Zhichao Lian und 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.
Der volle Inhalt der QuelleHu, Cong, Kai Xu, Zhengqiu Zhu, Long Qin und 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.
Der volle Inhalt der QuelleXue, Tianfang, und Haibin Yu. „Unbiased Model-Agnostic Metalearning Algorithm for Learning Target-Driven Visual Navigation Policy“. Computational Intelligence and Neuroscience 2021 (08.12.2021): 1–12. http://dx.doi.org/10.1155/2021/5620751.
Der volle Inhalt der QuelleMoskalenko, 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.
Der volle Inhalt der QuelleSchmidt, Henri, Palash Sashittal und Benjamin J. Raphael. „A zero-agnostic model for copy number evolution in cancer“. PLOS Computational Biology 19, Nr. 11 (09.11.2023): e1011590. http://dx.doi.org/10.1371/journal.pcbi.1011590.
Der volle Inhalt der QuelleHasan, 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.
Der volle Inhalt der QuelleLabaien Soto, Jokin, Ekhi Zugasti Uriguen und 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.
Der volle Inhalt der QuelleSun, Yifei, Cheng Song, Feng Lu, Wei Li, Hai Jin und 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.
Der volle Inhalt der QuelleWu, Gang, Junjun Jiang, Kui Jiang und 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.
Der volle Inhalt der QuelleLi, Ding, Yan Liu und 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.
Der volle Inhalt der QuelleChen, Mingyang, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z. Pan und 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.
Der volle Inhalt der QuelleShozu, 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, Nr. 12 (17.12.2020): 1691. http://dx.doi.org/10.3390/biom10121691.
Der volle Inhalt der QuelleAlinia, Parastoo, Asiful Arefeen, Zhila Esna Ashari, Seyed Iman Mirzadeh und 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.
Der volle Inhalt der QuelleApicella, A., F. Isgrò, R. Prevete und 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.
Der volle Inhalt der QuelleDiprose, William K., Nicholas Buist, Ning Hua, Quentin Thurier, George Shand und 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.
Der volle Inhalt der QuelleLiu, Jiakang, und Hua Huo. „DFENet: Double Feature Enhanced Class Agnostic Counting Methods“. Frontiers in Computing and Intelligent Systems 6, Nr. 1 (01.12.2023): 70–76. http://dx.doi.org/10.54097/fcis.v6i1.14.
Der volle Inhalt der QuelleMoon, Jae-pil, Jin-Guk Kim, Choong-Heon Yang und 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.
Der volle Inhalt der QuelleLi, Jolen, Christoforos Galazis, Larion Popov, Lev Ovchinnikov, Tatyana Kharybina, Sergey Vesnin, Alexander Losev und 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.
Der volle Inhalt der QuelleR, 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.
Der volle Inhalt der QuelleTOPCU, 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.
Der volle Inhalt der QuelleVieira, Carla Piazzon Ramos, und Luciano Antonio Digiampietri. „A study about Explainable Articial Intelligence: using decision tree to explain SVM“. Revista Brasileira de Computação Aplicada 12, Nr. 1 (08.01.2020): 113–21. http://dx.doi.org/10.5335/rbca.v12i1.10247.
Der volle Inhalt der QuelleNoviandy, Teuku Rizky, Ghalieb Mutig Idroes, Irsan Hardi, Mohd Afjal und 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.
Der volle Inhalt der QuelleThakur, 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.
Der volle Inhalt der QuelleGunel, Kadir, und 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.
Der volle Inhalt der QuelleKedar, 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 (06.06.2024): 1–5. http://dx.doi.org/10.55041/ijsrem35556.
Der volle Inhalt der QuelleGilo, Daniel, und 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.
Der volle Inhalt der QuelleSong, Rui, Fausto Giunchiglia, Yingji Li, Mingjie Tian und 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.
Der volle Inhalt der QuelleWang, Kewei, Yizheng Wu, Zhiyu Pan, Xingyi Li, Ke Xian, Zhe Wang, Zhiguo Cao und 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.
Der volle Inhalt der QuelleLin, Weiping, Zhenfeng Zhuang, Lequan Yu und 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.
Der volle Inhalt der QuelleRonchetti, Franco, Facundo Quiroga, Ulises Jeremias Cornejo Fandos, Gastón Gustavo Rios, Pedro Dal Bianco, Waldo Hasperué und Laura Lanzarini. „comparison of small sample methods for Handshape Recognition“. Journal of Computer Science and Technology 23, Nr. 1 (03.04.2023): e03. http://dx.doi.org/10.24215/16666038.23.e03.
Der volle Inhalt der QuelleDemertzis, Konstantinos, und Lazaros Iliadis. „GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification“. Algorithms 13, Nr. 3 (07.03.2020): 61. http://dx.doi.org/10.3390/a13030061.
Der volle Inhalt der QuelleFan, Xinchen, Lancheng Zou, Ziwu Liu, Yanru He, Lian Zou und 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.
Der volle Inhalt der QuelleSaarela, Mirka, und 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.
Der volle Inhalt der QuelleWinder, Isabelle, und 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.
Der volle Inhalt der QuelleAkhtar, Naveed, und 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.
Der volle Inhalt der QuellePruthi, Danish, Rachit Bansal, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C. Lipton, Graham Neubig und 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.
Der volle Inhalt der QuelleMentias, 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, Nr. 15 (11.04.2023): 1121–33. http://dx.doi.org/10.1161/circulationaha.122.061995.
Der volle Inhalt der QuelleYarlagadda, Sri Kalyan, Daniel Mas Montserrat, David Güera, Carol J. Boushey, Deborah A. Kerr und Fengqing Zhu. „Saliency-Aware Class-Agnostic Food Image Segmentation“. ACM Transactions on Computing for Healthcare 2, Nr. 3 (Juli 2021): 1–17. http://dx.doi.org/10.1145/3440274.
Der volle Inhalt der QuelleSun, Chenyu, Hangwei Qian und 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.
Der volle Inhalt der QuellePetrescu, Livia, Cătălin Petrescu, Ana Oprea, Oana Mitruț, Gabriela Moise, Alin Moldoveanu und Florica Moldoveanu. „Machine Learning Methods for Fear Classification Based on Physiological Features“. Sensors 21, Nr. 13 (01.07.2021): 4519. http://dx.doi.org/10.3390/s21134519.
Der volle Inhalt der QuelleRangwala, Murtaza, Jun Liu, Kulbir Singh Ahluwalia, Shayan Ghajar, Harnaik Singh Dhami, Benjamin F. Tracy, Pratap Tokekar und Ryan K. Williams. „DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets“. Agronomy 11, Nr. 11 (05.11.2021): 2245. http://dx.doi.org/10.3390/agronomy11112245.
Der volle Inhalt der QuelleChristensen, Cade, Torrey Wagner und 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.
Der volle Inhalt der QuelleYamazawa, 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.
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