Neural Architecture Search: A Survey

Thomas Elsken , Jan Hendrik Metzen , Frank Hutter

Authors on Pith no claims yet

classification 📊 stat.ML cs.LGcs.NE

keywords searchneuralarchitecturearchitecturesprogressrecognitionstrategyaccording

read the original abstract

Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect for this progress are novel neural architectures. Currently employed architectures have mostly been developed manually by human experts, which is a time-consuming and error-prone process. Because of this, there is growing interest in automated neural architecture search methods. We provide an overview of existing work in this field of research and categorize them according to three dimensions: search space, search strategy, and performance estimation strategy.

This paper has not been read by Pith yet.

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

RELO: Reinforcement Learning to Localize for Visual Object Tracking
cs.CV 2026-05 unverdicted novelty 6.0

RELO replaces handcrafted spatial priors with a reinforcement learning policy for target localization in visual tracking and reports 57.5% AUC on LaSOText without template updates.
Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models
cs.CL 2026-01 unverdicted novelty 6.0

NCoTS treats chain-of-thought reasoning as a search problem and uses a dual-factor heuristic to find paths that are over 3.5% more accurate and 22% shorter on benchmarks.