Differentiate the Evaluator, Not the Program: An Efficient Runtime Representation for Neuro-Symbolic Learning
📰 ArXiv cs.AI
Learn to differentiate the evaluator, not the program, for efficient neuro-symbolic learning and improved parameter calibration
Action Steps
- Implement a neuro-symbolic learning framework using a library like PyTorch or TensorFlow
- Use a runtime representation to differentiate the evaluator, not the program
- Apply gradient-based optimization to the evaluator to calibrate parameters
- Test the approach on a dataset to evaluate its efficiency and effectiveness
- Compare the results with traditional methods to assess the improvement
Who Needs to Know This
Researchers and engineers working on neuro-symbolic learning and AI systems can benefit from this approach to improve the efficiency of their models and reduce the bottleneck of parameter calibration
Key Insight
💡 Differentiating the evaluator, not the program, can significantly improve the efficiency of neuro-symbolic learning and reduce the bottleneck of parameter calibration
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🤖 Differentiate the evaluator, not the program, for efficient neuro-symbolic learning! 🚀
Key Takeaways
Learn to differentiate the evaluator, not the program, for efficient neuro-symbolic learning and improved parameter calibration
Full Article
Title: Differentiate the Evaluator, Not the Program: An Efficient Runtime Representation for Neuro-Symbolic Learning
Abstract:
arXiv:2607.03574v1 Announce Type: cross Abstract: AI systems increasingly propose executable scientific models whose value depends on both their symbolic structure and their fitted continuous parameters. This makes parameter calibration the bottleneck of program-and-parameter co-search: an outer loop can generate thousands of candidate programs, but each needs an inner gradient-based optimization before it can be assessed. Staging each candidate into its own differentiable graph makes individual
Abstract:
arXiv:2607.03574v1 Announce Type: cross Abstract: AI systems increasingly propose executable scientific models whose value depends on both their symbolic structure and their fitted continuous parameters. This makes parameter calibration the bottleneck of program-and-parameter co-search: an outer loop can generate thousands of candidate programs, but each needs an inner gradient-based optimization before it can be assessed. Staging each candidate into its own differentiable graph makes individual
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