Bridging Structured Knowledge and Data: A Unified Framework with Finance Applications
Researchers propose Structured-Knowledge-Informed Neural Networks (SKINNs), a unified framework that combines structured knowledge and data for estimation tasks, with applications in finance
- Develop a unified estimation framework that embeds theoretical and structural insights as differentiable constraints within neural networks
- Jointly estimate neural network parameters and structural parameters in a single optimization problem
- Enforce theoretical consistency on observed data using SKINNs
- Apply SKINNs to finance applications, such as predicting stock prices or credit risk
Data scientists and AI engineers on a team can benefit from SKINNs as it allows them to incorporate domain knowledge into neural network models, while product managers can leverage this framework to develop more accurate and interpretable models for financial applications
💡 SKINNs can incorporate domain knowledge into neural network models, improving their accuracy and interpretability
💡 Introducing SKINNs: a unified framework that combines structured knowledge and data for estimation tasks #AI #Finance
Key Takeaways
Researchers propose Structured-Knowledge-Informed Neural Networks (SKINNs), a unified framework that combines structured knowledge and data for estimation tasks, with applications in finance
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Abstract:
arXiv:2604.00987v1 Announce Type: cross Abstract: We develop Structured-Knowledge-Informed Neural Networks (SKINNs), a unified estimation framework that embeds theoretical, simulated, previously learned, or cross-domain insights as differentiable constraints within flexible neural function approximation. SKINNs jointly estimate neural network parameters and economically meaningful structural parameters in a single optimization problem, enforcing theoretical consistency not only on observed data
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