Web23 mei 2024 · This approach was recently extended in [42], for energy-efficient edge classification with reliability guarantees, in [43] for ensemble inference at the edge, and in [36] by incorporating the... Web7 jul. 2024 · DOI: 10.1109/GCWkshps52748.2024.9682062 Corpus ID: 235765752; In-network Learning for Distributed Training and Inference in Networks @article{Moldoveanu2024InnetworkLF, title={In-network Learning for Distributed Training and Inference in Networks}, author={Matei Moldoveanu and Abdellatif Zaidi}, …
Information bottlenecks and dimensionality reduction in …
Web8 feb. 2024 · This paper investigates task-oriented communication for edge inference, where a low-end edge device transmits the extracted feature vector of a local data … Theory of Information Bottleneck is recently used to study Deep Neural Networks (DNN). Consider and respectively as the input and output layers of a DNN, and let be any hidden layer of the network. Shwartz-Ziv and Tishby proposed the information bottleneck that expresses the tradeoff between the mutual information measures and . In this case, and respectively quantify the amount of information that the hidden layer contains about the input and the output. They conje… cchs covid hotline
Learning Task-Oriented Communication for Edge Inference: An …
Web8 feb. 2024 · This paper investigates task-oriented communication for edge inference, where a low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing. It is critical to encode the data into an informative and compact representation for low-latency inference given the limited bandwidth. Web8 feb. 2024 · This paper investigates task-oriented communication for edge inference, where a low-end edge device transmits the extracted feature vector of a local data sample … bus times number