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Found 3 papers, 1 papers with code
Resolution and Relevance Trade-offs in Deep Learning
The fraction of inputs that are associated to the same state is a natural measure of similarity and is simply related to the cost in bits required to represent these inputs.
Causality inference in stochastic systems from neurons to currencies: Profiting from small sample size
We introduce a data-driven statistical physics approach to model inference based on minimizing a free energy of data and show superior model recovery for small sample sizes.
Data Analysis, Statistics and Probability Quantitative Methods
Minimal Perceptrons for Memorizing Complex Patterns
We developed a new complexity measure of binary patterns, and estimated the minimal network size for memorizing them as a function of their complexity.
