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Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

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楼主: LH941
发表于 2025-3-30 11:58:34 | 显示全部楼层
Two-Stage Multilayer Perceptron Hawkes Processron Hawkes Process (TMPHP). The model consists of two types of multilayer perceptrons: one that applies MLPs (learning features of each event sequence to capture long-term dependencies between different events) independently for each event sequence, and one that applies MLPs to different event seque
发表于 2025-3-30 15:26:00 | 显示全部楼层
Hawkes Process via Graph Contrastive Discriminant Representation Learning and Transformer Capturing ntrastive Discriminant representation Learning and Transformer capturing long-term dependencies(GCDRLT) is the two-stage pipeline to enhance the capacity of hidden representation both on long-term dependencies and discriminant feature extraction. Experimental results on multiple datasets validate th
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Data Representation and Clustering with Double Low-Rank Constraints the rank of input data and representation coefficients to extract the multi-subspace structure underlying the observed data. The experimental results show that the proposed method has superior performance in the clustering experiments for each dataset.
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发表于 2025-3-31 06:55:23 | 显示全部楼层
O,GPT: A Guidance-Oriented Periodic Testing Framework with Online Learning, Online Testing, and Onli the rationality of all selected questions. Finally, to set up the online feedback, we test O.GPT on an on-line simulated environment which can model qualitative development of knowledge proficiency. The results of our experiment conducted on two well-established student response datasets indicate t
发表于 2025-3-31 09:14:49 | 显示全部楼层
AFFSRN: Attention-Based Feature Fusion Super-Resolution Networkutational cost and thus improve the network’s performance, we propose the novel deep feature fusion group (DFFG) for feature fusion. Experimental results show that this method achieves a better peak signal-to-noise ratio (PSNR) and computation overhead than the existing super-resolution algorithms.
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