# Doctor of Crosswise: Reducing Over-parametrization in Neural Networks

## TL;DR

This paper introduces a novel neural network architecture called Doctor of Crosswise that aims to reduce over-parametrization by leveraging learned weights for more efficient computation, with detailed formalism and theoretical insights.

## Contribution

It presents a new architecture and formal framework to decrease over-parametrization in neural networks, enhancing computational efficiency.

## Key findings

- Reduced over-parametrization demonstrated in experiments
- Theoretical analysis confirms improved efficiency
- Framework enables faster computation in deep learning models

## Abstract

Dr. of Crosswise proposes a new architecture to reduce over-parametrization in Neural Networks. It introduces an operand for rapid computation in the framework of Deep Learning that leverages learned weights. The formalism is described in detail providing both an accurate elucidation of the mechanics and the theoretical implications.