Search papers, labs, and topics across Lattice.
This paper critiques McCoy & Griffiths' (2025) claim that their method distills Bayesian priors into artificial neural networks using Model-Agnostic Meta-Learning (MAML). The authors argue that the standard interpretation of a prior is not fulfilled, as the method only initializes weights favorably without altering the objective function. Furthermore, they reveal that while MAML can approximate some empirical results of Bayesian learning, it ultimately leads to overfitting and poor generalization on unseen data compared to true Bayesian learners.
M&M's analysis reveals that M&G's approach may mislead researchers about the true capabilities of MAML in capturing Bayesian learning principles.
McCoy&Griffiths (2025, henceforth M&G) suggest that a Bayesian prior can be distilled into Artificial Neural Networks (ANNs) through Model-Agnostic Meta-Learning (MAML, Finn et al., 2017). They support this empirically by showing that meta-trained networks demonstrate formal language learning abilities comparable to Yang&Piantadosi (2023)'s Bayesian learner, significantly outperforming standard ANNs. We point out that under the standard interpretation of a prior, M&G's procedure does not actually instill one; it merely initializes network weights favorably, leaving the objective function unchanged. We then consider a more permissive interpretation, where the system as a whole can be seen as implementing a Bayesian learner even without an explicit prior in the objective. We show that this interpretation faces nontrivial challenges. Finally, we assess how well MAML approximates the empirical results of Bayesian learning, showing that unlike genuine Bayesian learners, M&G's model overfits and generalizes poorly to unseen data.
