Titlebook: Database Systems for Advanced Applications; 28th International C Xin Wang,Maria Luisa Sapino,Hongzhi Yin Conference proceedings 2023 The Ed

CHASE

Masahisa Fujita,Jacques-Fran?ois Thisseat the unlabeled set as a substitute for normal samples and ignore the potential anomalies in it, which fails make full use of the abnormal supervision information. To address this issue, we propose a .eta-.seudo-label based framework for .nomaly .etection (MPAD). The framework strives to obtain eff

ARCH

Masahisa Fujita,Jacques-Fran?ois Thisseto detect outliers in more than two views. Moreover, they only employ the clustering technique to detect outliers in a multi-view scenario. Besides, the relationships among different views are not fully utilized. To address the above issues, we propose ECMOD for learning .nhanced representations via

Canyon

Yair Mundlak,Donald Larson,Al Cregoe performance, their performance drops dramatically when adapting to the new domain and under few-shot scenarios. One reason is that the huge gap in semantic space between different domains makes the model obtain suboptimal representations in the new domain. The other is the inability to learn class

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adulterant

Timothy M. Smeeding,Peter Gottschalkty and improving user experience in a task-oriented dialogue system. The key challenge is how to learn discriminative intent representations that are beneficial for distinguishing in-domain (IND) and OOD intents. However, previous methods ignore the compactness between instances and dispersion among

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https://doi.org/10.1007/978-1-349-26188-8ot all data owners (or keepers) could develop feasible learning models for knowledge discovery’s sake. Oftentimes, the original data need to be passed to or shared with researchers or data scientists for better mining insights, especially in the medical, financial, and industrial fields. However, co

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The Prehistory of Chaotic Economic Dynamicsls is becoming marginal. Instead, we argue that the improvement can be achieved by using traffic-related facts or laws, which is termed exogenous knowledge. To this end, we propose a knowledge-driven memory system that can be seamlessly integrated into GCN-based traffic forecasting models. Specifica

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coagulation