Zobrazeno 1 - 10
of 198
pro vyhledávání: '"Yang, Huiyuan"'
To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML). However, there are some critical challenges for FL deployment. One
Externí odkaz:
http://arxiv.org/abs/2403.06653
This paper studies the joint data and semantics lossy compression problem, i.e., an extension of the hidden lossy source coding problem that entails recovering both the hidden and observable sources. We aim to study the nonasymptotic and second-order
Externí odkaz:
http://arxiv.org/abs/2402.02501
This paper studies a joint data and semantics lossy compression problem in the finite blocklength regime, where the data and semantic sources are correlated, and only the data source can be observed by the encoder. We first introduce an information-t
Externí odkaz:
http://arxiv.org/abs/2401.14962
Satellite Internet of Things (IoT) is to use satellites as the access points for IoT devices to achieve the global coverage of future IoT systems, and is expected to support burgeoning IoT applications, including communication, sensing, and computing
Externí odkaz:
http://arxiv.org/abs/2312.01336
Electrocardiogram (ECG) is an essential signal in monitoring human heart activities. Researchers have achieved promising results in leveraging ECGs in clinical applications with deep learning models. However, the mainstream deep learning approaches u
Externí odkaz:
http://arxiv.org/abs/2310.00818
Data augmentation is a common practice to help generalization in the procedure of deep model training. In the context of physiological time series classification, previous research has primarily focused on label-invariant data augmentation methods. H
Externí odkaz:
http://arxiv.org/abs/2309.09970
Publikováno v:
ICCV 2023
Contrastive learning has shown promising potential for learning robust representations by utilizing unlabeled data. However, constructing effective positive-negative pairs for contrastive learning on facial behavior datasets remains challenging. This
Externí odkaz:
http://arxiv.org/abs/2304.00058
Due to individual heterogeneity, person-specific models are usually achieving better performance than generic (one-size-fits-all) models in data-driven health applications. However, generic models are usually preferable in real-world applications, du
Externí odkaz:
http://arxiv.org/abs/2302.09126
Due to individual heterogeneity, performance gaps are observed between generic (one-size-fits-all) models and person-specific models in data-driven health applications. However, in real-world applications, generic models are usually more favorable du
Externí odkaz:
http://arxiv.org/abs/2211.12422