Learning Task-General Representations with Generative Neuro-Symbolic Modeling (2006.14448v2)

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.

Analyzing Generative Neuro-Symbolic Models for Task-General Representation Learning

The paper "Learning Task-General Representations with Generative Neuro-Symbolic Modeling" proposes a novel approach to reconcile the strengths and address the limitations of symbolic and neural network models. The core contribution is a Generative Neuro-Symbolic (GNS) model that integrates symbolic methodologies with neural networks to create rich, compositional conceptual representations. This model is primarily evaluated on the Omniglot challenge, which involves task-general representation learning from handwritten characters—a domain requiring high-level compositional and causal understanding.

Overview of the GNS Model

The GNS model addresses critical limitations of existing models by adopting a hierarchical approach that includes type-level and token-level representations, factoring into the joint distribution of character types and exemplars. The type-level model uses a probabilistic generative process with symbolic strokes and a neural network-based renderer to capture correlations and compositional detail. Meanwhile, the token-level model introduces noise to create realistic variability among instances of a character type.

Empirical Evaluation

The efficacy of the GNS model is tested on the Omniglot dataset, which challenges models to perform human-like concept learning through one-shot classification, parsing, generating new exemplars, and generating new concepts. Notably, the GNS model demonstrates significant versatility:

1. One-shot Classification: The model achieves a low error rate of 5.7% across 20-way within-alphabet classification episodes, closely approaching the symbolic Bayesian Program Learning (BPL) benchmark and surpassing several neural architecture-based models.
2. Parsing and Generative Tasks: The model excelled in parsing tasks, producing parsings that aligned well with human intuition and varying character styles. For generating new examples, the GNS model captured structure faithfully, invoking strong reliability in producing plausible handwritten samples, as evidenced by qualitative assessments.
3. Unconditional Concept Generation: In this task, the conceptual creativity of the GNS model was apparent. It produced novel character concepts showcasing diverse and coherent styles, effectively utilizing the compositional understanding inherent in the model's design.

Theoretical Implications and Future Prospects

The GNS model contributes to ongoing research in AI by offering a route towards more adaptable and conceptually robust systems. It implies that blending probabilistic programs with neural networks could help bridge the gap between raw perceptual input processing and conceptual abstraction—key features of human-like understanding.

However, the current model relies on explicit compositional representations and moderate prior knowledge, hinting at constraints when extending to broader, less structured domains. Future work may focus on refining token-level models, potentially incorporating neural sub-routines more fully, thus enabling a more profound interaction between hierarchical levels for enhanced generalization capabilities. Future exploration could also test the applicability of GNS across varied conceptual domains beyond handwriting.

In summary, the proposed Generative Neuro-Symbolic Model delivers promising strides in developing task-general AI systems by encapsulating both structural and statistical faculties reminiscent of human cognition, specifically in highly structured tasks. This research marks a notable advance in marrying symbolic reasoning with neural scalability, setting a precedent for future endeavors in AI redolent of genuine human-like conceptual learning.