Titlebook: Advances in Knowledge Discovery and Data Mining; 28th Pacific-Asia Co De-Nian Yang,Xing Xie,Jerry Chun-Wei Lin Conference proceedings 2024

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James F. M. Meaney,John Sheehan,Mathias Boosge of 2%, while diversity increases by 3.4% when compared with advanced prompt learning-based methods. Additionally, KiProL is 45% faster than the state-of-the-art knowledgeable, prompt learning method in training efficiency.

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Magnetic Substrates of T1 Relaxation,resentations in a controlled manner that guides the generation of query completions towards non-toxicity. We evaluate toxicity levels in the generated completions across two real-world datasets using two classifiers: a publicly available (Detoxify) and a search query-specific classifier which we dev

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Magnetic Substrates of T2 Relaxation, to a broader context, significantly enriching the model’s interpretative fidelity. Our rigorous experiments on a suite of benchmark datasets have underscored TCGNN’s proficiency, outperforming extant GNN-based models. This validates our premise that an adept synthesis of text clustering within a GN

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Fast or Turbo Spin Echo Imaging,tperform the baseline methods in terms of accuracy and F1 scores on four benchmark online review datasets. Further, we show that the proposed methods can be extended with multiple adaptations and demonstrate a qualitative analysis of the proposed approach using sample text for aspect term extraction

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Regurgitation