Constructive Approximation of Random Process via Stochastic Interpolation Neural Network Operators
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Abstract (arXiv)
In this paper, we construct a class of stochastic interpolation neural network operators (SINNOs) with random coefficients activated by sigmoidal functions. We establish their boundedness, interpolation accuracy, and approximation capabilities in the mean square sense, in probability, as well as path-wise within the space of second-order stochastic (random) processes ( L2(Omega, F,mathbbP) ). Additionally, we provide quantitative error estimates using the modulus of continuity of the processes. These results highlight the effectiveness of SINNOs for approximating stochastic processes with potential applications in COVID-19 case prediction.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 35 references that are in the map. First posted on arXiv 30 Dec 2025.
Builds on
5 of 35 references.
References · 5
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
- 2023Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networkscited by 14
- 2023Refined Kolmogorov Complexity of Analog, Evolving and Stochastic Recurrent Neural Networkscited by 1
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Citing papers per year: 2026: 1.
1 paper.