A NEW APPROACH TO BUILDING ENERGY MODELS OF NEURAL NETWORKS - Parzhyn, Yurii; Lapin, Mykyta; Bokhan, Kostiantyn (Сучасні інформаційні системи, 2025)
Relevance. Modern artificial neural network models require significant energy and other resources for training and operation. Training generative models involves vast amounts of data. At the same time, these models face challenges related to the trustworthiness of the information they generate. An alternative to current paradigms of building and training neural networks is the development of energy-based models, which could potentially overcome these shortcomings and bring information processing closer to biologically and physically grounded processes. However, existing energy-based models differ little from classical models in terms of their limitations and drawbacks. Therefore, developing new approaches to modeling energy-based information processing in neural networks is highly relevant. The object of research is the process of information processing in artificial neural networks. The subject of the research is the mathematical models for the construction and training of artificial neural networks. The purpose of this paper is to develop and experimentally validate a theoretical framework that postulates the energetic nature of information and its role in the self-organization and evolution of complex information systems. Research Results. A fundamental theory is proposed, describing information as a structure of perceived external energy parameters that govern the processes of forming the internal energetic structure of a system—its model of the external world. This theory encompasses concepts of energy landscapes, principles of energy-based structural and parametric reduction, and a critical analysis of existing computational paradigms. Experimental studies on the construction and training of the developed energy-based model confirm its high generalization ability in one-pass training without using the backpropagation algorithm on ultra-small training datasets.