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Supervised structure learning.

Karl J Friston1, Lancelot Da Costa2, Alexander Tschantz3

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Summary
This summary is machine-generated.

This study introduces a novel Bayesian approach for discovering discrete generative models by prioritizing data ingestion order. The method uses expected free energy to guide model selection, enhancing structure learning for complex tasks.

Keywords:
Active inferenceActive learningBayesian model selectionDisentanglementExpected free energyPlanning as inferenceStructure learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Structure learning is crucial for understanding discrete generative models.
  • Bayesian model selection offers a principled framework for learning.
  • The order of data assimilation can significantly impact model discovery.

Purpose of the Study:

  • To develop a Bayesian method for structure learning in discrete generative models.
  • To investigate the role of data ingestion order in model selection.
  • To utilize expected free energy for guiding model discovery.

Main Methods:

  • Employing Bayesian model selection with priors on model selection based on expected free energy.
  • Reformulating expected free energy as constrained mutual information.
  • Applying the scheme to image classification (MNIST) and dynamic model discovery (sprite-based disentanglement, Tower of Hanoi).

Main Results:

  • Demonstrated effective image classification on the MNIST dataset.
  • Successfully discovered models with dynamics in visual disentanglement and Tower of Hanoi tasks.
  • Generative models were constructed autodidactically to recover factorial structures and dynamics.

Conclusions:

  • The proposed Bayesian framework effectively performs structure learning for discrete generative models.
  • Prioritizing data ingestion order via expected free energy enhances model discovery.
  • The method shows promise for complex tasks involving latent state recovery and dynamics.