Distilling Symbolic Priors for Concept Learning into Neural Networks (2402.07035v1)

Abstract: Humans can learn new concepts from a small number of examples by drawing on their inductive biases. These inductive biases have previously been captured by using Bayesian models defined over symbolic hypothesis spaces. Is it possible to create a neural network that displays the same inductive biases? We show that inductive biases that enable rapid concept learning can be instantiated in artificial neural networks by distilling a prior distribution from a symbolic Bayesian model via meta-learning, an approach for extracting the common structure from a set of tasks. By generating the set of tasks used in meta-learning from the prior distribution of a Bayesian model, we are able to transfer that prior into a neural network. We use this approach to create a neural network with an inductive bias towards concepts expressed as short logical formulas. Analyzing results from previous behavioral experiments in which people learned logical concepts from a few examples, we find that our meta-trained models are highly aligned with human performance.

• The paper distills Bayesian inductive biases into neural networks using meta-learning, achieving rapid concept generalization with a 0.95 correlation to human performance.
• The methodology employs the Rational Rules model to generate training tasks that transfer symbolic priors into neural network architectures.
• The approach offers scalable applications in domains like robotics by mimicking human reasoning and enabling efficient learning from limited data.

Distilling Symbolic Priors for Concept Learning into Neural Networks

The paper "Distilling Symbolic Priors for Concept Learning into Neural Networks" by Marinescu et al. explores the integration of symbolic Bayesian models into neural networks for concept learning. This research addresses the long-standing challenge of enabling artificial neural networks to learn efficiently from a small number of examples, akin to human learners, by leveraging inductive biases typically associated with symbolic representations.

Overview

The authors propose a methodology to impart inductive biases inherent in symbolic Bayesian models into neural networks using meta-learning. They construct neural networks with a preference for concepts formulated as short logical formulas. By utilizing meta-learning, they transfer the prior distribution from a Bayesian model into a neural network, thereby aligning the inductive biases of the network with those encoded symbolically.

Methodology

The approach employs a probabilistic Bayesian model to define a target inductive bias. Specifically, the Rational Rules model, which uses context-free grammar to generate concept definitions with probabilistic expansions, serves as the backbone for generating training tasks for the neural network. These tasks are then employed in a meta-learning framework, specifically Model-Agnostic Meta-Learning (MAML), to train a neural network that implicitly learns the Bayesian model's prior distribution. Meta-learning here is used to enable the network to achieve the same rapid generalization capabilities observed in humans by learning the common structure across numerous related tasks.

Key Findings

Several experiments validate the efficacy of this approach. Notably, the model proficiently replicates human-like generalization behaviors previously studied in psychological experiments, with a notable 0.95 correlation in some settings against human performance metrics. The results indicated that the prior-trained neural network closely approximates both human data and the Bayesian model predictions, outperforming standard neural networks that lack this symbolic prior incorporation.

The evaluation includes comparisons against benchmark concept learning datasets, showcasing that the meta-trained models can emulate human concept learning more effectively by mimicking inductive biases symbolically understood by humans due to the distilled Bayesian priors.

Implications and Future Directions

This research marks a promising direction toward the convergence of symbolic and connectionist approaches to cognitive modeling. While Bayesian models have been successful in capturing human concept learning inductive biases, this work demonstrates how those biases can be distilled into scalable neural networks that do not rely on symbolic task representations.

One practical implication of this research is for domains requiring rapid learning from limited data, such as robotics or personal assistants, where human-like learning efficiency can reduce the need for expansive datasets. Theoretically, this paper pushes the boundary of how machines can approximate cognitive processes akin to that of human reasoning and perception by utilizing structured symbolic insight within the flexible architecture of neural networks.

For future research, one area of exploration is incorporating more complex priors, possibly combining several inductive biases beyond the current scope, such as shape or mutual exclusivity biases, to further enhance the human-like reasoning capabilities in neural architectures. There’s also scope for exploring how this methodology can be scaled to more complex task environments involving multi-modal and real-world data streams.

Overall, this paper provides a compelling synthesis of symbolic reasoning and neural network processing, setting a foundation for further interdisciplinary research bridging cognitive science and artificial intelligence.