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Found 4 papers, 1 papers with code
Timely Communications for Remote Inference
In addition, we design low-complexity scheduling policies to improve inference performance.
How Does Data Freshness Affect Real-time Supervised Learning?
To minimize the inference error in real-time, we propose a new "selection-from-buffer" model for sending the features, which is more general than the "generate-at-will" model used in earlier studies.
A Local Geometric Interpretation of Feature Extraction in Deep Feedforward Neural Networks
In this paper, we present a local geometric analysis to interpret how deep feedforward neural networks extract low-dimensional features from high-dimensional data.
The Age of Correlated Features in Supervised Learning based Forecasting
In these applications, a neural network is trained to predict a time-varying target (e. g., solar power), based on multiple correlated features (e. g., temperature, humidity, and cloud coverage).
