Correlational Training of Morphological Neural Networks

The paper introduces a new weight update method for morphological neural networks based on the Multiplicative Weights Update scheme. It treats each morphological perceptron as a learning-from-experts problem in logarithmic space and uses a correlation-based reward that favors inputs aligned with the desired output change, even when gradient signals are weak. Experiments on fully connected layers and transformer components across nine benchmarks show that correlational training improves performance on eight tasks, up to 32.84 percentage points, and reduces run‑to‑run variability.

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• llms
• benchmarks