We proposed a self-renewal ML (SRML) approach for the optimization of wireless network capacity. The SRML method incrementally improves the throughput maximization of future optimization instances through the design of a data selection algorithm for fine-tuning an application identification model. Our proposed SRML method reduces the computational complexity and achieves a higher solution efficiency.
We developed a three-stage data-driven approach to distill the hidden information about COVID-19. The first stage employs a Bayesian network structure learning method to identify the causal relationships among COVID-19 symptoms and their intrinsic demographic variables. As a second stage, the output from the Bayesian network structure learning, serves as a useful guide to train an unsupervised machine learning (ML) algorithm that uncovers the similarities in patients’ symptoms through clustering. The final stage then leverages the labels obtained from clustering to train a demographic symptom identification model which predicts a patient’s symptom class and the corresponding demographic probability distribution.
In this project, we study the classic wireless multi-commodity network flow problem, where the network-layer routing issue and link-layer scheduling issue are coupled together. We proposed a self-supervised learning paradigm in which the scheduling structures (referring to the independent sets) of historical optimization experiences from a fixed network topology are smartly exploited to facilitate solving new instances with greatly reduced computational overhead.
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
Published:
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Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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