CL-NOTEARS: Continuous Optimization Algorithm Based on Curriculum Learning Framework
Causal structure learning plays a crucial role in the current field of artificial intelligence, yet existing causal structure learning methods are susceptible to interference from data sample noise and often become trapped in local optima. To address these challenges, this paper introduces a continu...
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| Veröffentlicht in: | Mathematics (Basel) Jg. 12; H. 17; S. 2640 |
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| Abstract | Causal structure learning plays a crucial role in the current field of artificial intelligence, yet existing causal structure learning methods are susceptible to interference from data sample noise and often become trapped in local optima. To address these challenges, this paper introduces a continuous optimization algorithm based on the curriculum learning framework: CL-NOTEARS. The model utilizes the curriculum loss function during training as a priority evaluation metric for curriculum selection and formulates the sample learning sequence of the model through task-level curricula, thereby enhancing the model’s learning performance. A curriculum-based sample prioritization strategy is employed that dynamically adjusts the training sequence based on variations in loss function values across different samples throughout the training process. The results demonstrate a significant reduction in the impact of sample noise in the data, leading to improved model training performance. |
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| AbstractList | Causal structure learning plays a crucial role in the current field of artificial intelligence, yet existing causal structure learning methods are susceptible to interference from data sample noise and often become trapped in local optima. To address these challenges, this paper introduces a continuous optimization algorithm based on the curriculum learning framework: CL-NOTEARS. The model utilizes the curriculum loss function during training as a priority evaluation metric for curriculum selection and formulates the sample learning sequence of the model through task-level curricula, thereby enhancing the model’s learning performance. A curriculum-based sample prioritization strategy is employed that dynamically adjusts the training sequence based on variations in loss function values across different samples throughout the training process. The results demonstrate a significant reduction in the impact of sample noise in the data, leading to improved model training performance. |
| Author | Li, Xuan Huang, Hongbin Zhou, Yun Xiao, Kaiming Liu, Kaiyue Zhang, Hang Liu, Lihua |
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| Cites_doi | 10.1007/BF00994016 10.1109/BHI50953.2021.9508538 10.1007/s10208-022-09581-9 10.1007/s11859-015-1084-y 10.1007/s11227-021-04097-5 10.1007/s10115-009-0239-6 10.1214/aos/1176344136 10.1109/TMM.2021.3136717 10.1109/BIBM47256.2019.8983151 10.1145/1273496.1273604 10.7551/mitpress/1754.001.0001 10.1007/BF00356088 10.1145/1553374.1553380 10.1016/j.patrec.2011.03.003 10.1017/S0266466603004109 10.1109/TCBB.2016.2591526 10.1126/science.1173299 10.1137/1.9781611977172.48 10.1093/biomet/ast043 10.1051/ijmqe/2023009 10.2331/fishsci.60.411 10.1016/j.ins.2016.01.090 10.1126/science.1105809 10.1137/0916069 |
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| SubjectTerms | Accuracy Algorithms Artificial intelligence casual structure Continuity (mathematics) continuous optimization Curricula curriculum learning Decision making Gaussian cluster Heuristic Learning Machine learning Methods Optimization Optimization algorithms Performance evaluation Random variables |
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| Title | CL-NOTEARS: Continuous Optimization Algorithm Based on Curriculum Learning Framework |
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