CoRA-NAS is a two‑stage neural architecture search framework that first uses a static coarse ranking (CoRA‑Rank) based on capacity and structure‑at‑initialization proxies, then refines this ranking with low‑cost learning‑curve extrapolation (CoRA‑Refine) using anchor samples and an ExtraTrees residual model. The method achieves high Spearman correlations across multiple NAS benchmarks and selects architectures that approach the best‑known accuracy with only about 1% of the training cost, without relying on fully trained labels for ranking. It demonstrates robust cross‑space performance and improves over static capacity proxies, especially in size‑only search spaces.
By Yifan Yang, Zhaoyan Wang, Zheng Gao, Xiaoyu Li, Jiaojiao Jiang
RiPPLE is a method for ranking neural architectures across an entire search space using only a small fraction of early training data. It treats partial training as labels for a limited set of anchor architectures, extrapolates their learning curves, and propagates these surrogate labels to other architectures without requiring per‑candidate features. The approach is evaluated on twelve benchmark cells from four search‑space families and the larger DARTS space, demonstrating its effectiveness in ranking quality, label efficiency, and architecture selection.
By Yifan Yang, Zhaoyan Wang, Zheng Gao, Xiaoyu Li, Jiaojiao Jiang
The paper introduces PPNAS, a prediction‑powered inference method for neural architecture search that combines a small set of accurately evaluated architectures with a large set of zero‑cost proxy (ZCP) evaluations. PPNAS uses the ordinal information from ZCPs to generate pairwise ranking supervision and applies a debiasing step to reconcile discrepancies between proxy and true performance rankings. Experiments show that PPNAS achieves state‑of‑the‑art results in predictor‑based NAS under limited evaluation budgets.
By Pascal Janetzky, Yuxin Wang, Michael Klar, Stefan Feuerriegel
arXiv:2609.14184v1 Announce Type: new
Abstract: Neural Architecture Search (NAS) automates network design, but evaluating a single candidate requires training it to convergence, making exhaustive sea...
By Hassan Touayouch, Rabie Najem, Mohammed Benjelloun
arXiv:2608.21927v1 Announce Type: cross
Abstract: Zero-cost proxies enable neural architecture search (NAS) to rank candidate networks from statistics computed at initialization, avoiding repeated tr...
By Yutao Lai, Kezhao Lai, Hai-Lin Liu
arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.
By Selen Erkan, Bastian Boll, Kristian Kersting, Bj\"orn Deiseroth, Letitia Parcalabescu