Meta-learning as a bridge between neural networks and symbolic Bayesian models.
Journal:
The Behavioral and brain sciences
Published Date:
Sep 23, 2024
Abstract
Meta-learning is even more broadly relevant to the study of inductive biases than Binz et al. suggest: Its implications go beyond the extensions to rational analysis that they discuss. One noteworthy example is that meta-learning can act as a bridge between the vector representations of neural networks and the symbolic hypothesis spaces used in many Bayesian models.