Uncertainty estimation plays a key role in many practical areas such as simulation and parameter optimization. Gaussian process (GP) is one popular model that provides naturally well-calibrated uncertainty estimates. However, it is challenging to learn a global GP posterior under the federated learning (FL) framework. In FL, clients’ private data should not be shared, but merging local kernels directly leads to privacy leakage. Previous works that consider federated GPs avoid it and focus on the personalized setting. This sacrifices information from other clients that can be exploited to benefit generalization. We present Federated Bayesian Neural Regression (FedBNR) that learns a global federated GP while respecting clients’ privacy. We incorporate deep kernel learning and random features by defining a unifying random kernel (URK). URK enables a principled approach of learning a global posterior as if all client data is centralized. Experiments conducted on real world regression datasets show statistically significant improvements.

Infant cry detection is a crucial component of baby care system. In this paper, we propose a lightweight and robust method for infant cry detection. The method leverages blueprint separable convolutions to reduce computational complexity, and a time-frequency recurrent neural network for adaptive denoising. The overall framework of the method is structured as a multi-scale convolutional recurrent neural network, which is enhanced by efficient spatial attention mechanism and contrast-aware channel attention module, and acquire local and global information from the input feature of log Mel-spectrogram. Multiple public datasets are adopted to create a diverse and representative dataset, and environmental corruption techniques are used to generate the noisy samples encountered in real-world scenarios. Results show that our method exceeds many state-of-the-art methods in accuracy, F1-score, and complexity under various signal-to-noise ratio conditions. The code is at https://github.com/fhfjsd1/ICD_MMSP.

RDSQ: Reliable Queue Protocol over Shared Logs Share on Author: Haolin Yu University of Waterloo, Waterloo, ON, Canada University of Waterloo, Waterloo, ON, CanadaView Profile Authors Info & Affiliations SIGMOD '18: Proceedings of the 2018 International Conference on Management of DataMay 2018 Pages 1821–1823https://doi.org/10.1145/3183713.3183718Published:27 May 2018 0citation170DownloadsMetricsTotal Citations0Total Downloads170Last 12 Months11Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access