Editing Neural Architecture Search (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Neural Architecture Search (NAS)''' — Automated discovery of neural network architectures using optimization algorithms.
* '''Search space''' — The set of all possible architectures considered; defined by a human as cells, operations, connections, and their ranges.
* '''Search strategy''' — The algorithm used to explore the search space: random, evolutionary, reinforcement learning, gradient-based.
* '''Performance estimation''' — How a candidate architecture's performance is estimated without full training; enables faster search.
* '''Cell''' — A basic building block repeated in the final architecture; NAS often searches for optimal cells rather than full networks.
* '''DARTS (Differentiable Architecture Search)''' — A gradient-based NAS that makes the architecture selection differentiable using a continuous relaxation.
* '''Evolutionary NAS''' — Uses evolutionary algorithms (mutation, crossover, selection) to search architecture space.
* '''RL-based NAS''' — Uses a controller trained with reinforcement learning to sample and evaluate architectures (original NAS paper, Zoph & Le 2017).
* '''One-shot NAS''' — Trains a single large "supernet" containing all candidate architectures as sub-networks; evaluates sub-architectures by inheriting weights from the supernet.
* '''Supernet''' — A network containing all candidate architectures as subgraphs; weights are shared across sub-architectures.
* '''Hardware-aware NAS''' — Optimizes for both accuracy and hardware efficiency (FLOPs, latency, memory) simultaneously.
* '''EfficientNet''' — A highly efficient CNN family discovered by NAS (compound scaling) that achieved state-of-the-art accuracy/efficiency tradeoffs.
* '''NASNet''' — One of the first high-profile NAS results; discovered by RL-based search on CIFAR-10 and transferred to ImageNet.
* '''ProxylessNAS''' — A memory-efficient NAS method that directly searches on the target task and hardware.
* '''AutoML''' — Broader automation of the ML pipeline including NAS, HPO, and feature engineering.
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