Research of efficient MNIST feature transformations, research zkML-friendly ML models for it, implement in transpiler and examples

The goal is to further improve MNIST performance (classification accuracy and constraint usage). For this, the following milestones need to be completed:

Milestone 1, feature pre-processing techniques

• Task: Research feature transformations that transform the MNIST images into lower-dimensional features with rich information
• Deliverable: Python code that performs feature pre-processing, experimental results how valuable the different techniques are for MNIST classification using common ML models
• Date: Thu, 9/28

Milestone 2, zkML-friendly ML model exploration

• Task: Research ML models that can classify these features well while being zkml-friendly
• Deliverable: Prototypical implementation of these models in Leo, estimation of constraint size
• Date: 10/4

Milestone 3, implementation of further models in the transpiler

• Task: Implement one (or more) promising models in the zkml Leo transpiler, run MNIST tests with these models
• Deliverable: Updated python code for the transpiler, Jupyter notebook running MNIST in Leo with the updated transpiler
• Date: 10/18