3 citations · 3 across the 4 of their papers we have counts for
4 papers
Insights from the Use of Previously Unseen Neural Architecture Search Datasets
Rob Geada, David Towers, Matthew Forshaw +2
The boundless possibility of neural networks which can be used to solve a problem -- each with different performance -- leads to a situation where a Deep Learning expert is require…
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics
Rob Geada, Andrew Stephen McGough
Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a vari…
TrustyAI Explainability Toolkit
Rob Geada, Tommaso Teofili, Rui Vieira +2
Artificial intelligence (AI) is becoming increasingly more popular and can be found in workplaces and homes around the world. The decisions made by such "black box" systems are oft…
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners
Rob Geada, Dennis Prangle, Andrew Stephen McGough
One-shot Neural Architecture Search (NAS) aims to minimize the computational expense of discovering state-of-the-art models. However, in the past year attention has been drawn to t…