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New EM-NeSy approach enhances neurosymbolic AI learning

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced EM-NeSy, a novel approach to neurosymbolic learning that frames the process as an instance of the Expectation-Maximization (EM) algorithm. This method allows for approximate inference without requiring the symbolic component to be differentiable, a common limitation in current state-of-the-art models. EM-NeSy updates neural parameters using gradient descent based on a computed posterior, demonstrating scalability and computational efficiency in experiments. AI

IMPACT This research offers a more flexible and efficient method for training neurosymbolic AI models, potentially improving robustness and interpretability.

RANK_REASON The cluster contains an academic paper detailing a new method for neurosymbolic learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New EM-NeSy approach enhances neurosymbolic AI learning

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Annegret Seibt, Luc De Raedt, Giuseppe Marra ·

    EM-NeSy: Expectation Maximization for Neurosymbolic Learning

    arXiv:2606.14463v1 Announce Type: new Abstract: Neurosymbolic (NeSy) models integrate neural networks and symbolic reasoning for robust and interpretable AI. State-of-the-art NeSy models require that the symbolic component is expressed in a differentiable way, often complicating …

  2. arXiv cs.LG TIER_1 English(EN) · Giuseppe Marra ·

    EM-NeSy: Expectation Maximization for Neurosymbolic Learning

    Neurosymbolic (NeSy) models integrate neural networks and symbolic reasoning for robust and interpretable AI. State-of-the-art NeSy models require that the symbolic component is expressed in a differentiable way, often complicating the use of approximate inference. We propose EM-…

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