Search Results for author:
Found 3 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.
A Scalable and Cloud-Native Hyperparameter Tuning System
In this paper, we introduce Katib: a scalable, cloud-native, and production-ready hyperparameter tuning system that is agnostic of the underlying machine learning framework.
