Conference paper “Can Adaptive Diffusion Networks Do Better with Less Data?”

22 July 2024
Renato
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Categories: Research

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.

Leia também:

  1. Paper “Reducing the Communication and Computational Cost of Random Fourier Features Kernel LMS in Diffusion Networks” published in ICASSP 2023
  2. Paper “Adaptive diffusion networks: An overview”
  3. New paper: An Adaptive Algorithm for Sampling over Diffusion Networks with Dynamic Parameter Tuning and Change Detection Mechanisms
  4. New paper: A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks
  5. New paper: A Sampling Algorithm for Diffusion Networks

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