23 citations · 73 across the 19 of their papers we have counts for
19 papers
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…
Causal Confirmation Measures: From Simpson's Paradox to COVID-19
Chenguang Lu
When we compare the influences of two causes on an outcome, if the conclusion from every group is against that from the conflation, we think there is Simpson's Paradox. The Existin…
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…
Understanding and Accelerating EM Algorithm's Convergence by Fair Competition Principle and Rate-Verisimilitude Function
Chenguang Lu
Why can the Expectation-Maximization (EM) algorithm for mixture models converge? Why can different initial parameters cause various convergence difficulties? The Q-L synchronizatio…
The P-T Probability Framework for Semantic Communication, Falsification, Confirmation, and Bayesian Reasoning
Chenguang Lu
Many researchers want to unify probability and logic by defining logical probability or probabilistic logic reasonably. This paper tries to unify statistics and logic so that we ca…