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Found 5 papers, 2 papers with code
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
We show that IB learning is, in fact, equivalent to a special class of the quantization problem.
Neural Entropic Estimation: A faster path to mutual information estimation
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.
Aggregated Learning: A Deep Learning Framework Based on Information-Bottleneck Vector Quantization
Based on the notion of information bottleneck (IB), we formulate a quantization problem called "IB quantization".
Agglomerative Info-Clustering
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.
Duality between Feature Selection and Data Clustering
The feature-selection problem is formulated from an information-theoretic perspective.
