May 20, 2024Proceedings of the AAAI Symposium SeriesOpen Access

Modeling Patterns for Neural-Symbolic Reasoning Using Energy-based Models

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Neural-symbolic integration improves performance by up to 37% over standard neural baselines in semi-supervised tasks and 19% over GPT-4 on question-answering.
An energy-based framework provides a knowledge representation-agnostic taxonomy that captures reasoning with probability, formal logic, and arithmetic constraints.
Derived gradient expressions for learning losses formalize the neural-symbolic interface, enabling reliable commonsense reasoning across diverse machine learning tasks.

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Implication

Empirical analysis reveals up to a 37% improvement over neural baselines across reasoning tasks, highlighting the power of unified neural-symbolic interfaces.

Cite This Study

Dickens et al. (2024) studied this question.

synapsesocial.com/papers/68e694bdb6db64358761b661 https://doi.org/10.1609/aaaiss.v3i1.31187

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Implication

Empirical analysis reveals up to a 37% improvement over neural baselines across reasoning tasks, highlighting the power of unified neural-symbolic interfaces.

Modeling Patterns for Neural-Symbolic Reasoning Using Energy-based Models

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Implication

Cite This Study

Modeling Patterns for Neural-Symbolic Reasoning Using Energy-based Models

Key Points

Discussion

Implication

Cite This Study

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