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Titlebook: Quality Innovation and Sustainability; 3rd ICQIS, Aveiro Un Jo?o Carlos de Oliveira Matias,Carina Maria Olivei Conference proceedings 2023

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11#
發(fā)表于 2025-3-23 10:17:14 | 只看該作者
Samruddha Kokare,Radu Godina,Jo?o Pedro Oliveiractive approach called RAP, which employs a two-stage learning framework. Specifically, in the first stage, we construct a weighted bipartite graph to model interaction’s confidence-score, which effectively blocks the spread of noise information in GNN. Furthermore, in?the second stage, RAP introduce
12#
發(fā)表于 2025-3-23 16:03:33 | 只看該作者
13#
發(fā)表于 2025-3-23 19:59:30 | 只看該作者
Mariana Inácio Marques,Ana Afonso Alcantara,Gabriel Guerreiro e Carreira,Jo?o Caldeira Heitorand separately; The second module introduces the interact-KNN method to effectively discern probable interaction pairs between a protein and a ligand. These pairs are then classified into distinct types based on their distance for more representative interaction embedding. The third module comprehen
14#
發(fā)表于 2025-3-23 23:59:49 | 只看該作者
15#
發(fā)表于 2025-3-24 03:30:03 | 只看該作者
Palash Saha,Subrata Talapatra,José Carlos Sá,Gilberto Santosaud detection and sequence-based recommendation. In this paper, we propose SeqSHAP to explain sequential model predictions from a unique perspective. Compared to existing element-wise XAI methods, SeqSHAP provides intuitive explanations at the subsequence level, which explicitly models the effect of
16#
發(fā)表于 2025-3-24 09:04:21 | 只看該作者
Tiago Rodrigues-Sa,Manuel Au-Yong-Oliveirae a cost-aware fact selection algorithm to guide the fact update process. We also generate explainable rules to find the dependency with the fact update to minimize the cost. We have conducted extensive experiments on real KBs. The experimental results verify the effectiveness and efficiency of our
17#
發(fā)表于 2025-3-24 11:00:54 | 只看該作者
Ana Carolina Cosenza,Gilberto Santos,Luis Cesar Ferreira Motta Barbosa termed as ., which combines POI data and road network data to generate the distribution of traffic flows. Our model has two novel modules: a graph reconstruction module and a POI supervised contrastive module. The graph reconstruction module includes a .-NN graph builder and a .-NN graph aggregator
18#
發(fā)表于 2025-3-24 16:33:57 | 只看該作者
19#
發(fā)表于 2025-3-24 19:21:44 | 只看該作者
20#
發(fā)表于 2025-3-25 01:58:35 | 只看該作者
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