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Related Experiment Video

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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Published on: March 3, 2023

Supervised Learning for Dynamical System Learning.

Ahmed Hefny¹, Carlton Downey¹, Geoffrey J Gordon¹

¹Carnegie Mellon University, Pittsburgh, PA 15213.

Advances in Neural Information Processing Systems

|January 10, 2017

Summary

This study introduces a new framework for learning dynamical systems by framing it as a sequence of supervised learning problems. This approach allows for easier incorporation of prior knowledge, improving state representation learning.

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Area of Science:

Dynamical Systems and Machine Learning
Computational Science

Background:

Spectral methods are popular for learning dynamical systems due to their efficiency.
However, these methods present challenges in incorporating prior information like sparsity or structure.

Purpose of the Study:

To present a novel framework for learning dynamical systems.
To enable the integration of prior knowledge using standard supervised learning techniques.

Main Methods:

Re-framing dynamical system learning as a series of ordinary supervised learning problems.
Utilizing techniques like L1 regularization for incorporating prior knowledge.
Demonstrating the framework with linear and non-linear regression.

Main Results:

Existing spectral methods are shown to be special cases of this new framework.
Non-linear regression and LASSO yield improved state representations compared to linear regression.
The framework's correctness is supported by general analysis.

Conclusions:

The proposed framework offers a flexible and effective approach to learning dynamical systems.
It facilitates the incorporation of prior knowledge, enhancing the learning process and state representation.