EVEN
发表于 2025-3-23 12:29:16
Fritz K. Kneubühlically employ a single structure augmentation to generate contrastive views. Recent research suggests feature augmentation-adding uniform noise perturbations in the feature space-as a replacement for structure augmentation in contrastive learning. This augmentation can mitigate popularity bias and a
宫殿般
发表于 2025-3-23 16:06:08
Fritz K. Kneubühltract user preferences from interaction records, they frequently neglect the user’s logical requirements, which are embedded in the logical relations between items and entities. Existing methods that account for user’s logical requirements employ neural networks to mimic logical operators, failing t
bisphosphonate
发表于 2025-3-23 19:02:39
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Monolithic
发表于 2025-3-24 01:32:11
Fritz K. Kneubühle of applications and potentially high value. In contrast to the conventional time series prediction tasks, the intrinsic characteristics of stocks render the incorporation of additional information a crucial factor in the prediction of stock movements. Inter-stock relationships and financial texts
Abutment
发表于 2025-3-24 04:15:00
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Overdose
发表于 2025-3-24 07:41:47
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胶水
发表于 2025-3-24 14:13:25
Fritz K. Kneubühlstructure has made significant progress in the field of time series forecasting, its forecasting performance for wind power data is a concern due to the high variability and stochasticity of short-term wind power data. To address this issue, this study proposes a novel wind power forecasting model,
Legion
发表于 2025-3-24 15:55:54
Fritz K. Kneubühllely on the features of individual nodes. Recent advancements in graph-based methods allow for the consideration of features across related nodes, enhancing predictive accuracy. Especially, Graph Neural Networks (GNNs) have shown high performance on graph-based fraud detection tasks. However, it pre
废除
发表于 2025-3-24 21:20:16
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脆弱么
发表于 2025-3-24 23:59:20
me that supports complex computation and has better performance than BFV. This gives CKKS an advantage when applied to machine learning. However, existing secure inference frameworks based on homomorphic encryption are mainly adopted in BFV or BGV, as these schemes have more batching slots than CKKS