{"ID":2892806,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00869","arxiv_id":"2508.00869","title":"Discrete approach to machine learning","abstract":"The article explores an encoding and structural information processing approach using sparse bit vectors and fixed-length linear vectors. The following are presented: a discrete method of speculative stochastic dimensionality reduction of multidimensional code and linear spaces with linear asymptotic complexity; a geometric method for obtaining discrete embeddings of an organised code space that reflect the internal structure of a given modality. The structure and properties of a code space are investigated using three modalities as examples: morphology of Russian and English languages, and immunohistochemical markers. Parallels are drawn between the resulting map of the code space layout and so-called pinwheels appearing on the mammalian neocortex. A cautious assumption is made about similarities between neocortex organisation and processes happening in our models.","short_abstract":"The article explores an encoding and structural information processing approach using sparse bit vectors and fixed-length linear vectors. The following are presented: a discrete method of speculative stochastic dimensionality reduction of multidimensional code and linear spaces with linear asymptotic complexity; a geom...","url_abs":"https://arxiv.org/abs/2508.00869","url_pdf":"https://arxiv.org/pdf/2508.00869v1","authors":"[\"Dmitriy Kashitsyn\",\"Dmitriy Shabanov\"]","published":"2025-07-19T11:39:54Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.ET\",\"cs.IT\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}