Methods › General › Neural Architecture Search › PNAS
Progressive Neural Architecture Search
PNAS
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.
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.
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Barrier-Free Microhabitats: Self-Organized Seclusion in Microbial Communities 27 Mar 2025 · 1 repository · arXiv:2503.21621
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Mixed Delay/Nondelay Embeddings Based Neuromorphic Computing with Patterned Nanomagnet Arrays 5 Dec 2024 · 0 repositories · arXiv:2412.04622
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Fluctuations and the limit of predictability in protein evolution 2 Dec 2024 · 0 repositories · arXiv:2412.01969
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From Complexity to Clarity: How AI Enhances Perceptions of Scientists and the Public's Understanding of Science 23 Apr 2024 · 0 repositories · arXiv:2405.00706
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Cell motility modes are selected by the interplay of mechanosensitive adhesion and membrane tension 31 May 2023 · 0 repositories · arXiv:2306.00236
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Convergence of the Backward Deep BSDE Method with Applications to Optimal Stopping Problems 8 Oct 2022 · 0 repositories · arXiv:2210.04118
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POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique 6 Oct 2022 · 0 repositories · arXiv:2210.02959
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Stochastic Gradient Descent and Anomaly of Variance-flatness Relation in Artificial Neural Networks 11 Jul 2022 · 0 repositories · arXiv:2207.04932
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Information Cocoons in Online Navigation 14 Sep 2021 · 0 repositories · arXiv:2109.06589
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Learning Interaction Kernels for Agent Systems on Riemannian Manifolds 30 Jan 2021 · 0 repositories · arXiv:2102.00327
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Stabilizing Deep Tomographic Reconstruction 4 Aug 2020 · 0 repositories · arXiv:2008.01846
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On the Transcriptomic Signature and General Stress State Associated with Aneuploidy 28 Jul 2020 · 0 repositories · arXiv:2007.14585
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Weak SINDy For Partial Differential Equations 6 Jul 2020 · 2 repositories · arXiv:2007.02848
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Sparse Identification of Nonlinear Dynamical Systems via Reweighted ℓ₁-regularized Least Squares 27 May 2020 · 0 repositories · arXiv:2005.13232
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Auto-Keras: An Efficient Neural Architecture Search System 27 Jun 2018 · 13 repositories · arXiv:1806.10282Syntology ran 3 of 6 samples · 3 unverified
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Progressive Neural Architecture Search 2 Dec 2017 · 18 repositories · arXiv:1712.00559Syntology ran 3 of 3 samples · 0 unverified · 2 pointer-only (licence)
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.
Usage over time archive 2025-07-28
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
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