Mutual Information Estimation
37 papers with code • 0 benchmarks • 0 datasets
To estimate mutual information from samples, specially for high-dimensional variables.
Benchmarks
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Latest papers with no code
Artifact Feature Purification for Cross-domain Detection of AI-generated Images
We also find that the artifact features APN focuses on across generators and scenes are global and diverse.
Mutual Information Estimation via Normalizing Flows
We propose a novel approach to the problem of mutual information (MI) estimation via introducing normalizing flows-based estimator.
On regularized Radon-Nikodym differentiation
We discuss the problem of estimating Radon-Nikodym derivatives.
Subgraph Networks Based Contrastive Learning
In addition, we also investigate the impact of the second-order subgraph augmentation on mining graph structure interactions, and further, propose a contrastive objective that fuses the first-order and second-order subgraph information.
On the Effectiveness of Hybrid Mutual Information Estimation
Estimating the mutual information from samples from a joint distribution is a challenging problem in both science and engineering.
Assessing Neural Network Representations During Training Using Data Diffusion Spectra
We also see that there is an increase in DSMI with the class label over time.
Computing high-dimensional optimal transport by flow neural networks
Flow-based models are widely used in generative tasks, including normalizing flow, where a neural network transports from a data distribution $P$ to a normal distribution.
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
We show that if these auxiliary densities are constructed such that they overlap with $p$ and $q$, then a multi-class logistic regression allows for estimating $\log p/q$ on the domain of any of the $K+2$ distributions and resolves the distribution shift problems of the current state-of-the-art methods.
Copula Density Neural Estimation
Probability density estimation from observed data constitutes a central task in statistics.
Representation Learning with Information Theory for COVID-19 Detection
Successful data representation is a fundamental factor in machine learning based medical imaging analysis.