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Mutual Information Estimation

To estimate mutual information from samples, specially for high-dimensional variables.

Papers

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Showing 26–50 of 91 papers

TitleStatusHype
Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization—0
Matching Text with Deep Mutual Information Estimation—0
Meta-Statistical Learning: Supervised Learning of Statistical Inference—0
Mixed vine copulas as joint models of spike counts and local field potentials—0
Multi-label Contrastive Predictive Coding—0
Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization—0
Exploiting Style and Attention in Real-World Super-Resolution—0
A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation—0
Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification—0
An Empirical Study of Leading Measures of Dependence—0
A Perspective on Neural Capacity Estimation: Viability and Reliability—0
A Quantitative Metric for Privacy Leakage in Federated Learning—0
A Reverse Jensen Inequality Result with Application to Mutual Information Estimation—0
Artifact Feature Purification for Cross-domain Detection of AI-generated Images—0
Biphasic Learning of GANs for High-Resolution Image-to-Image Translation—0
Causal Order Identification to Address Confounding: Binary Variables—0
CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection—0
Compressed Predictive Information Coding—0
Data-Driven Representations for Testing Independence: Modeling, Analysis and Connection with Mutual Information Estimation—0
Decomposed Mutual Information Estimation for Contrastive Representation Learning—0
Deep Spectral Clustering using Dual Autoencoder Network—0
Disentangled Noisy Correspondence Learning—0
Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation—0
Equitability Analysis of the Maximal Information Coefficient, with Comparisons—0
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression—0
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