In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …

In artificial intelligence systems, a question on how to express the uncertainty in knowledge
remains an open issue. The negation scheme provides a new perspective to solve this …

We present a review on the recent advances and emerging opportunities around the theme
of analyzing deep neural networks (DNNs) with information-theoretic methods. We first …

Despite their great success in practical applications, there is still a lack of theoretical and
systematic methods to analyze deep neural networks. In this paper, we illustrate an …

We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model
that is able to learn a Granger causal graph from a multivariate time series x and …

Traditional classification algorithms suppose that the sample distribution among classes is
balanced. Yet, such an assumption leads to biased performance over the majority class …

Cross-modal hashing that leverages hash functions to project high-dimensional data from
different modalities into the compact common hamming space, has shown immeasurable …

This paper proposes an Information Bottleneck theory based filter pruning method that uses
a statistical measure called Mutual Information (MI). The MI between filters and class labels …

There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-
network based psychiatric diagnosis, which, in turn, also motivates an urgent need for …

A novel functional estimator for Rényi's α-entropy and its multivariate extension was recently
proposed in terms of the normalized eigenspectrum of a Hermitian matrix of the projected …