Learning Task-General Representations with Generative Neuro-Symbolic Modeling

TL;DR

This paper introduces a generative neuro-symbolic model that combines neural networks and symbolic reasoning to learn rich, compositional, and causal representations of handwritten characters directly from raw data, enabling human-level concept learning.

Contribution

The work presents a novel neuro-symbolic generative model that integrates probabilistic programming with neural components to learn from raw data and generalize across multiple tasks.

Findings

Successfully learned from raw data without strong abstractions

Generalized to four different tasks from a single example

Outperformed previous models in concept learning

Abstract

People can learn rich, general-purpose conceptual representations from only raw perceptual inputs. Current machine learning approaches fall well short of these human standards, although different modeling traditions often have complementary strengths. Symbolic models can capture the compositional and causal knowledge that enables flexible generalization, but they struggle to learn from raw inputs, relying on strong abstractions and simplifying assumptions. Neural network models can learn directly from raw data, but they struggle to capture compositional and causal structure and typically must retrain to tackle new tasks. We bring together these two traditions to learn generative models of concepts that capture rich compositional and causal structure, while learning from raw data. We develop a generative neuro-symbolic (GNS) model of handwritten character concepts that uses the control flow of a probabilistic program, coupled with symbolic stroke primitives and a symbolic image renderer, to represent the causal and compositional processes by which characters are formed. The distributions of parts (strokes), and correlations between parts, are modeled with neural network subroutines, allowing the model to learn directly from raw data and express nonparametric statistical relationships. We apply our model to the Omniglot challenge of human-level concept learning, using a background set of alphabets to learn an expressive prior distribution over character drawings. In a subsequent evaluation, our GNS model uses probabilistic inference to learn rich conceptual representations from a single training image that generalize to 4 unique tasks, succeeding where previous work has fallen short.