Context: Neural networks often require many parameters, leading to increased computational cost and risk of overfitting. Efficiently managing these parameters is crucial for building robust and scalable models.
Problem: Traditional neural networks with independent parameters can become computationally intensive and prone to overfitting, particularly with limited data.
Approach: This essay explores tied parameters, a technique where certain weights are shared…