Correlational Training of Morphological Neural Networks
The paper proposes a new weight update method inspired by Multiplicative Weights Update (MWU) to train morphological neural networks. Traditional first-order methods struggle with these networks due to sparse and uninformative gradient signals. The proposed correlation-based reward favors inputs aligned with desired output changes, leading to better performance and stability compared to standard back-propagation.