SETOL derives the HTSR layer quality metrics as integrated R-transforms of the layer spectral density, and proposes a determinant condition (ERG) as a marker of ideal learning.
Statistical physics, Bayesian inference and neural information processing
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abstract
Lecture notes from the course given by Professor Sara A. Solla at the Les Houches summer school on "Statistical physics of Machine Learning". The notes discuss neural information processing through the lens of Statistical Physics. Contents include Bayesian inference and its connection to a Gibbs description of learning and generalization, Generalized Linear Models as a controlled alternative to backpropagation through time, and linear and non-linear techniques for dimensionality reduction.
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cs.LG 1years
2025 1verdicts
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SETOL: A Semi-Empirical Theory of (Deep) Learning
SETOL derives the HTSR layer quality metrics as integrated R-transforms of the layer spectral density, and proposes a determinant condition (ERG) as a marker of ideal learning.