<!-- Title Slide -->
# Milestone 3 Presentation
![Zero-Knowledge Proofs](https://i.imgur.com/v9lyGdV.jpg)
---
<!-- Introduction Slide -->
## Pushing Boundaries 🚀
We've come a long way in our quest to enhance zero-knowledge proofs for Weightless Neural Networks (WNNs). Let's dive into the exciting technical achievements.
---
<!-- Rust Prover Section -->
## Rust Prover: Enhancing Efficiency
Our journey began by optimizing the Rust Prover, a key component in cryptographic protocols.
Our target:
**Reduce amount of constraints and increase overall efficientcy.**
---
### Small Adjustments We Tried
We have tried
- Changing the Hash Function
- Compression of Storage
- Removal of Redundant Layers
---
### Exploring Folding Schemes
- Investigated folding schemes like [Sangria](https://geometry.xyz/notebook/sangria-a-folding-scheme-for-plonk) and [Origami](https://hackmd.io/@aardvark/rkHqa3NZ2).
- Potential to reduce constraints but needs integration into Halo2 library.
---
### Innovative Lookup Compression Techniques
- Introduced a [custom compression scheme](https://github.com/zkp-gravity/optimisation-research/tree/main/lookup_compression).
- Achieved a 14-fold theoretical lookup table compression.
- Our sights set on a 30-fold improvement.
---
<!-- WNN Section -->
## WNN: Elevating Performance
We extended our research to enhance data preprocessing and feature selection for Weightless Neural Networks (WNNs).
---
### Unleashing Data Augmentation
- Leveraged data augmentation to combat overfitting and boost generalization.
- Caution required for smaller models.
- Larger models excel with increased pattern variety.
---
### Model Reduction through Feature Selection
- Developed a feature selection algorithm.
- Reduced model size by up to 50% with modest accuracy drops.
---
### Feature Selection for Precision
- Introduced the greedy algorithm.
- Effective for larger, complex datasets.
---
<!-- Future Directions Section -->
## Charting the Future
Our journey continues as we chart future directions in zero-knowledge proofs for WNNs.
---
### Improved Lookup Compression
- Focus on enhancing lookup compression algorithms like Lasso.
- Seamless integration with cryptographic libraries.
- Exploring novel compression techniques.
---
### Scaling Feature Selection
- Apply feature selection algorithms to larger, complex datasets.
- Evaluate performance and scalability beyond MNIST.
---
<!-- Conclusion Slide -->
## In Conclusion
Our journey is filled with challenges and innovations, all focused on advancing zero-knowledge proofs for Weightless Neural Networks.
---
<!-- Explore Our Research Slide -->
## Explore Our Research
- [Research Repository](https://github.com/zkp-gravity/optimisation-research/tree/main)
- [Detailed Research Writeup](https://github.com/zkp-gravity/optimisation-research/blob/main/writeup.pdf)
- [Implementation of Lookup Compression](https://github.com/zkp-gravity/optimisation-research/tree/main/lookup_compression)
For a deeper dive into our technical achievements, explore our research repository, read our detailed research writeup, and examine our lookup compression implementation.
---
<!-- Revisit Our Journey Slide -->
## Revisit Our Journey
To see where it all began, check out our [Initial Blog Post from the Hackathon](https://hackmd.io/@benjaminwilson/zero-gravity).
Thank you for joining us on this journey of exploration and innovation in privacy-preserving technologies.
{"title":"Milestone 3 Presentation","description":"Zero-Knowledge Proofs","contributors":"[{\"id\":\"57638455-1234-47cb-a685-1ef1d280e798\",\"add\":3484,\"del\":0}]"}