23 citations · 73 across the 19 of their papers we have counts for
8 papers · 1 filter
Reviewing Evolution of Learning Functions and Semantic Information Measures for Understanding Deep Learning
Chenguang Lu
A new trend in deep learning, represented by Mutual Information Neural Estimation (MINE) and Information Noise Contrast Estimation (InfoNCE), is emerging. In this trend, similarity…
How Semantic Information G Measure Relates to Distortion, Freshness, Purposiveness, and Efficiency
Chenguang Lu
To improve communication efficiency and provide more useful information, we need to measure semantic information by combining inaccuracy or distortion, freshness, purposiveness, an…
Using the Semantic Information G Measure to Explain and Extend Rate-Distortion Functions and Maximum Entropy Distributions
Chenguang Lu
In the rate-distortion function and the Maximum Entropy (ME) method, Minimum Mutual In-formation (MMI) distributions and ME distributions are expressed by Bayes-like formulas, in-c…
The Semantic Information Method for Maximum Mutual Information and Maximum Likelihood of Tests, Estimations, and Mixture Models
Chenguang Lu
It is very difficult to solve the Maximum Mutual Information (MMI) or Maximum Likelihood (ML) for all possible Shannon Channels or uncertain rules of choosing hypotheses, so that w…
Semantic Information Measure with Two Types of Probability for Falsification and Confirmation
Cheguang Lu
Logical Probability (LP) is strictly distinguished from Statistical Probability (SP). To measure semantic information or confirm hypotheses, we need to use sampling distribution (c…
GPS Information and Rate Tolerance - Clarifying Relationship between Rate Distortion and Complexity Distortion
Chenguang Lu
I proposed rate tolerance and discussed its relation to rate distortion in my book "A Generalized Information Theory" published in 1993. Recently, I examined the structure function…