Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Tensor decomposition and applications
Abstract
This paper introduces a novel dynamic mapping theory for non-linear matrices, aiming to facilitate machine learning algorithms through automated matrix adaptation and optimization. We propose a framework where the matrix's dynamic evolution is modeled as a physical process, allowing for learning and control of this evolution. The core mechanism focuses on establishing a dynamic mapping between the matrix's initial state and its subsequent states, leveraging techniques from dynamical systems and reinforcement learning to achieve optimal performance. This theory offers a promising alternative to traditional machine learning approaches, particularly in situations where data scarcity or complex dynamics are prevalent. This work demonstrates the potential of this approach to enhance the performance of machine learning models across various applications.
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A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
