Controlling Overfitting in Machine Learning with Physics
Author Information
Author(s): Kozyrev Sergei V., Lopatin Ilya A., Pechen Alexander N.
Primary Institution: Steklov Mathematical Institute of Russian Academy of Sciences
Hypothesis
Can analogies from physics and biology help explain and control overfitting in machine learning algorithms?
Conclusion
The study demonstrates that using physical analogies can effectively control overfitting in machine learning models.
Supporting Evidence
- Analogies from physics and biology can provide insights into machine learning theory.
- Stochastic gradient Langevin dynamics can help control overfitting.
- Generative adversarial networks can be interpreted through predator-prey dynamics.
Takeaway
This study shows that we can use ideas from physics and biology to help machine learning models learn better and avoid mistakes.
Methodology
The study uses analogies from physics and biology to explain overfitting control in machine learning, particularly through stochastic gradient Langevin dynamics and generative adversarial networks.
Limitations
The approach may not work for complex landscapes without clear minima and saddles.
Digital Object Identifier (DOI)
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