Neuro-Symbolic Geometric Abstraction (NeuSOGA): From Observations to Symbolic Mathematical Representations
A fundamental challenge in artificial intelligence is the transformation of observations into explicit symbolic representations suitable for abstraction, interpretation, and reasoning. While modern AI systems achieve remarkable perceptual capabilities through large-scale statistical learning, the resulting knowledge is typically encoded within latent parameters that are difficult to inspect or manipulate analytically. Inspired by Neuro-Symbolic AI and theories of human abstraction, this paper investigates the formation of symbolic mathematical representations from geometric observations. We
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- PossiblePossibly related (embedding) · 56%Steering machine reasoning with brain signals →
- PossiblePossibly related (embedding) · 56%The Emergent Symbolic Structure of Artificial Neural Networks →
- LinkedLinked via arxiv author · 85%Qingde Li →
“Neuro-Symbolic Geometric Abstraction (NeuSOGA): From Observations to Symbolic Mathematical Representations”
- LinkedLinked via arxiv author · 85%Qingqi Hong →
“Neuro-Symbolic Geometric Abstraction (NeuSOGA): From Observations to Symbolic Mathematical Representations”
- LinkedLinked via arxiv author · 85%Yunjie Tian →
“Neuro-Symbolic Geometric Abstraction (NeuSOGA): From Observations to Symbolic Mathematical Representations”
- PossiblePossibly related (embedding) · 47%Introducing my first Field Intelligence prototype, a new kind of ML architecture based on the physics of the mind [P] →