1 code implementation • 12 Jan 2020 • Masoumeh Soflaei, Hongyu Guo, Ali Al-Bashabsheh, Yongyi Mao, Richong Zhang
We show that IB learning is, in fact, equivalent to a special class of the quantization problem.
1 code implementation • 30 May 2019 • Chung Chan, Ali Al-Bashabsheh, Hing Pang Huang, Michael Lim, Da Sun Handason Tam, Chao Zhao
In particular, we show that MI-NEE reduces to MINE in the special case when the reference distribution is the product of marginal distributions, but faster convergence is possible by choosing the uniform distribution as the reference distribution instead.
no code implementations • 26 Jul 2018 • Hongyu Guo, Yongyi Mao, Ali Al-Bashabsheh, Richong Zhang
Based on the notion of information bottleneck (IB), we formulate a quantization problem called "IB quantization".
no code implementations • 18 Jan 2017 • Chung Chan, Ali Al-Bashabsheh, Qiaoqiao Zhou
An agglomerative clustering of random variables is proposed, where clusters of random variables sharing the maximum amount of multivariate mutual information are merged successively to form larger clusters.
no code implementations • 27 Sep 2016 • Chung Chan, Ali Al-Bashabsheh, Qiaoqiao Zhou, Tie Liu
The feature-selection problem is formulated from an information-theoretic perspective.