Built a Hybrid NAS tool for RNN architectures (HyNAS-R) – Looking for feedback for my final year evaluation [R]

Reddit r/MachineLearning / 4/7/2026

💬 OpinionTools & Practical UsageModels & Research

共有:

Key Points

A student is seeking feedback on HyNAS-R, a Neural Architecture Search tool aimed at automatically finding strong RNN architectures for NLP tasks.
The approach combines a zero-cost proxy (Hidden Covariance) with metaheuristic optimization to evaluate thousands of candidate architectures while avoiding expensive full training runs.
The system’s optimizer uses an Improved Grey Wolf Optimizer to guide the architecture search efficiently.
The author provides a video walkthrough of the core algorithm and tech stack, along with a feedback form intended to support the final-year evaluation process.

Hi everyone,

I'm currently in the evaluation phase of my Final Year Project and am looking for feedback on the system I've built. It's called HyNAS-R, a Neural Architecture Search tool designed to automatically find the best RNN architectures for NLP tasks by combining a zero-cost proxy with metaheuristic optimization.

I have recorded a video explaining the core algorithm and the technology stack behind the system, specifically how it uses an Improved Grey Wolf Optimizer and a Hidden Covariance proxy to search through thousands of architectures without expensive training runs.

Video Explanation: https://youtu.be/mh5kOF84vHY

If anyone is willing to watch the breakdown and share their thoughts, I would greatly appreciate it. Your insights will be directly used for my final university evaluation. Live demo link is inside the form for anyone interested.

Feedback Form: https://forms.gle/keLrigwSXBb74od7A

Thank you in advance for your time and feedback!

submitted by /u/PittuPirate
[link] [comments]