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Found 4 papers, 1 papers with code
Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learning
In this study, we present Targeted Gradient Descent (TGD), a novel fine-tuning method that can extend a pre-trained network to a new task without revisiting data from the previous task while preserving the knowledge acquired from previous training.
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.
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.
