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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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樓主: LH941
51#
發(fā)表于 2025-3-30 11:58:34 | 只看該作者
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
52#
發(fā)表于 2025-3-30 15:26:00 | 只看該作者
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
53#
發(fā)表于 2025-3-30 17:01:02 | 只看該作者
54#
發(fā)表于 2025-3-30 22:35:31 | 只看該作者
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.
55#
發(fā)表于 2025-3-31 02:20:16 | 只看該作者
56#
發(fā)表于 2025-3-31 06:55:23 | 只看該作者
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
57#
發(fā)表于 2025-3-31 09:14:49 | 只看該作者
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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