Skip to content
Sign in
Artwork for Eye on AI Weekly Research Watch
Eye on AI Weekly Research Watch · August 20 · 2 min

LP-NAS: Linear Programming-based Neural Architecture Search

Automating neural network design (NAS) is powerful but computationally costly, and differentiable NAS methods like DARTS often converge slowly. LP-NAS reframes the architecture search as a linear programming problem, using gradient and Hessian information to compute better-informed update directions that improve generalization while preserving optimized parameters. Its two variants integrate into the popular DARTS framework and show faster, stronger convergence on CIFAR-10/100, with transferability to ImageNet. This offers machine learning practitioners a more efficient, mathematically grounded alternative for automating model design across computer vision and beyond. Authors: Abhishek Shukla, Ankur Sinha, Faiz Hamid Paper: https://arxiv.org/abs/2608.14472v1

0:00-2:33
Play in

transcript

No transcript — this publisher did not publish one.

show notes

Automating neural network design (NAS) is powerful but computationally costly, and differentiable NAS methods like DARTS often converge slowly. LP-NAS reframes the architecture search as a linear programming problem, using gradient and Hessian information to compute better-informed update directions that improve generalization while preserving optimized parameters. Its two variants integrate into the popular DARTS framework and show faster, stronger convergence on CIFAR-10/100, with transferability to ImageNet. This offers machine learning practitioners a more efficient, mathematically grounded alternative for automating model design across computer vision and beyond.

Authors: Abhishek Shukla, Ankur Sinha, Faiz Hamid

Paper: https://arxiv.org/abs/2608.14472v1

more episodes

All episodes