法庭
发表于 2025-3-21 18:43:57
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彻底检查
发表于 2025-3-22 00:16:35
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飞镖
发表于 2025-3-22 01:14:33
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配偶
发表于 2025-3-22 06:22:08
https://doi.org/10.1007/978-981-97-4962-1 trade-off between accuracy and interpretability. Fuzzy Cognitive Maps (FCMs) and their extensions are recurrent neural networks that have been partially exploited towards fulfilling such a goal. However, the interpretability of these neural systems has been confined to the fact that both neural con
landmark
发表于 2025-3-22 12:06:29
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AMBI
发表于 2025-3-22 15:54:40
https://doi.org/10.1007/978-981-97-4962-1Belief Revision add/delete axioms or delete/add preconditions to rules, respectively. Reformation repairs them by changing the . of the faulty theory. Unfortunately, the ABC system overproduces repair suggestions. Our aim is to prune these suggestions to leave only a Pareto front of the optimal ones
prosthesis
发表于 2025-3-22 19:02:36
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Kindle
发表于 2025-3-23 00:49:32
https://doi.org/10.1007/978-981-97-4962-1prohibitive when tasked with creating models that are sensitive to personal nuances in human movement, explicitly present when performing exercises and when it is infeasible to collect training data to cover the whole target population. Accordingly, learning personalised models with few data remains
键琴
发表于 2025-3-23 02:36:51
https://doi.org/10.1007/978-981-97-4962-1ppens through trial and error using explorative methods, such as .-greedy. There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines. Model-based RL learns a model of the environment for learning the policy while model-free approac
全国性
发表于 2025-3-23 07:10:36
https://doi.org/10.1007/978-981-97-4962-1energy consumption constraints. Tsetlin Machines (TMs) are a recent approach to machine learning that has demonstrated significantly reduced energy usage compared to neural networks alike, while performing competitively accuracy-wise on several benchmarks. However, TMs rely heavily on energy-costly