Search Results for author:
Found 4 papers, 1 papers with code
Direct Federated Neural Architecture Search
In this paper, we present an effective approach for direct federated NAS which is hardware agnostic, computationally lightweight, and a one-stage method to search for ready-to-deploy neural network models.
Revisiting Neural Architecture Search
In this paper, instead of merely chasing slight improvements over state-of-the-art (SOTA) performance, we revisit the fundamental approach to NAS and propose a novel approach called ReNAS that can search for the complete neural network without much human effort and is a step closer towards AutoML-nirvana.
NASIB: Neural Architecture Search withIn Budget
Neural Architecture Search (NAS) represents a class of methods to generate the optimal neural network architecture and typically iterate over candidate architectures till convergence over some particular metric like validation loss.
Katib: A Distributed General AutoML Platform on Kubernetes
Automatic Machine Learning (AutoML) is a powerful mechanism to design and tune models.
