Conference paper “Can Adaptive Diffusion Networks Do Better with Less Data?”
In this paper, published with Dr. Daniel G. Tiglea, and Prof. Magno Silva, we analyze the performance of an algorithm for adaptive diffusion networks that controls the number of nodes sampled per iteration based on the estimation error. Our model shows that ceasing the sampling of the nodes when the estimation error is sufficiently low can slightly improve the steady-state performance.
The PDF file can be obtained in this page.
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