Review and Prospect of Algebraic Research in Equivalent Framework between Statistical Mechanics and Machine Learning Theory
<|reference_start|>Review and Prospect of Algebraic Research in Equivalent Framework between Statistical Mechanics and Machine Learning Theory: Mathematical equivalence between statistical mechanics and machine learning theory has been known since the 20th century, and researches based on such equivalence have provided novel methodology in both theoretical physics and statistical learning theory. For example, algebraic approach in statistical mechanics such as operator algebra enables us to analyze phase transition phenomena mathematically. In this paper, for theoretical physicists who are interested in artificial intelligence, we review and prospect algebraic researches in machine learning theory. If a learning machine has hierarchical structure or latent variables, then the random Hamiltonian cannot be expressed by any quadratic perturbation because it has singularities. To study an equilibrium state defined by such a singular random Hamiltonian, algebraic approach is necessary to derive asymptotic form of the free energy and the generalization error. We also introduce the most recent advance, in fact, theoretical foundation for alignment of artificial intelligence is now being constructed based on algebraic learning theory. This paper is devoted to the memory of Professor Huzihiro Araki who is a pioneer founder of algebraic research in both statistical mechanics and quantum field theory.<|reference_end|>
@article{watanabe2024review,
    title={Review and Prospect of Algebraic Research in Equivalent Framework
  between Statistical Mechanics and Machine Learning Theory},
    author={Sumio Watanabe},
    journal={arXiv preprint arXiv:2406.10234},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10234},
    primaryClass={cond-mat.stat-mech cs.LG math.ST stat.ML stat.TH}
}