Methods › General › Neural Architecture Search › PNAS

Progressive Neural Architecture Search

PNAS

16 papers tagged archive 2025-07-28

Introduced by Chenxi Liu et al. in Progressive Neural Architecture Search

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Progressive Neural Architecture Search, or PNAS, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to complex ones, pruning out unpromising structures as we go.

At iteration b of the algorithm, we have a set of K candidate cells (each of size b blocks), which we train and evaluate on a dataset of interest. Since this process is expensive, PNAS also learns a model or surrogate function which can predict the performance of a structure without needing to train it. We then expand the K candidates of size b into K′ ≫K children, each of size b+1. The surrogate function is used to rank all of the K′ children, pick the top K, and then train and evaluate them. We continue in this way until b=B, which is the maximum number of blocks we want to use in a cell.

PaperSource

Papers archive 2025-07-28

16 shown of 16, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 24 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Neural Architecture Search3
Adversarial Attack1
Articles1
AutoML1
Bayesian Optimization1
CPU1
Computed Tomography (CT)1
Diversity1
Evolutionary Algorithms1
Friction1
GPU1
General Classification1
Image Classification1
Image Reconstruction1
Language Modelling1
Large Language Model1
Recommendation Systems1
Reinforcement Learning1
Reinforcement Learning (RL)1
Relation1

Usage over time archive 2025-07-28

Papers per year tagged with PNAS: 2017 to 2025, peak 4 4 0 2017: 1 paper 2017 2018: 1 paper 2018 2019: 0 papers 2019 2020: 4 papers 2020 2021: 2 papers 2021 2022: 3 papers 2022 2023: 1 paper 2023 2024: 3 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (16 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Neural Architecture Search

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