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Polynomial Surrogate Training for Differentiable Ternary Logic Gate Networks
🤖AI Summary
Researchers introduce Polynomial Surrogate Training (PST) to enable differentiable ternary logic gate networks, reducing parameters by 2,187x while maintaining performance. The method extends beyond binary logic gates to ternary systems with an UNKNOWN state for uncertainty handling, training 2-3x faster than binary networks.
Key Takeaways
- →PST reduces ternary logic gate network parameters from 19,683 to 9 coefficients per neuron, a 2,187x reduction.
- →Ternary networks with UNKNOWN states enable principled abstention under uncertainty and train 2-3x faster than binary variants.
- →The method demonstrates Bayes-optimal uncertainty quantification on synthetic and tabular tasks.
- →Scaling experiments show the hardening gap contracts with overparameterization from 48K to 512K neurons.
- →PST methodology can extend to many-valued logic systems with only quadratic parameter growth.
#machine-learning#neural-networks#ternary-logic#uncertainty-quantification#parameter-efficiency#differentiable-programming#polynomial-surrogate#logic-gates
Read Original →via arXiv – CS AI
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