# FENAS: A Few-Shot Meta-Ensembling Framework for Efficient Neural Architecture Search with Synergized Zero-Cost Proxies

arXiv: https://arxiv.org/abs/2610.15307 · HTML: https://mlsanity.com/paper/2610.15307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 36 references that are in the map.
- First posted on arXiv 11 Oct 2026.

## Builds on (most cited first, 30 of 30)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Pruning neural networks without any data by iteratively conserving synaptic flow](https://mlsanity.com/paper/2006.05467/) (2020)
- [New insights and perspectives on the natural gradient method](https://mlsanity.com/paper/1412.1193/) (2014)
- [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://mlsanity.com/paper/2001.00326/) (2020)
- [A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets](https://mlsanity.com/paper/1707.08819/) (2017)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [On the Expressive Power of Deep Neural Networks](https://mlsanity.com/paper/1606.05336/) (2016)
- [Neural Architecture Search without Training](https://mlsanity.com/paper/2006.04647/) (2020)
- [Zero-Cost Proxies for Lightweight NAS](https://mlsanity.com/paper/2101.08134/) (2021)
- [BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search](https://mlsanity.com/paper/1910.11858/) (2019)
- [Understanding and Robustifying Differentiable Architecture Search](https://mlsanity.com/paper/1909.09656/) (2019)
- [Neural Architecture Optimization](https://mlsanity.com/paper/1808.07233/) (2018)
- [Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search](https://mlsanity.com/paper/1911.12126/) (2019)
- [Neural Predictor for Neural Architecture Search](https://mlsanity.com/paper/1912.00848/) (2019)
- [BRP-NAS: Prediction-based NAS using GCNs](https://mlsanity.com/paper/2007.08668/) (2020)
- [Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach](https://mlsanity.com/paper/1806.01316/) (2018)
- [How Powerful are Performance Predictors in Neural Architecture Search?](https://mlsanity.com/paper/2104.01177/) (2021)
- [Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?](https://mlsanity.com/paper/2006.06936/) (2020)
- [A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS](https://mlsanity.com/paper/2004.01899/) (2020)
- [Evaluating Efficient Performance Estimators of Neural Architectures](https://mlsanity.com/paper/2008.03064/) (2020)
- [ReNAS:Relativistic Evaluation of Neural Architecture Search](https://mlsanity.com/paper/1910.01523/) (2019)
- [Contrastive Neural Architecture Search with Neural Architecture Comparators](https://mlsanity.com/paper/2103.05471/) (2021)
- [AZ-NAS: Assembling Zero-Cost Proxies for Network Architecture Search](https://mlsanity.com/paper/2403.19232/) (2024)
- [CAP: A Context-Aware Neural Predictor for NAS](https://mlsanity.com/paper/2406.02056/) (2024)
- [NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance](https://mlsanity.com/paper/2408.08776/) (2024)
- [Evolutionary Neural Architecture Search with Dual Contrastive Learning](https://mlsanity.com/paper/2512.20112/) (2025)

## Abstract (arXiv)

Efficient neural architecture search (NAS) requires accurate performance estimation under limited evaluation budgets. Existing zero-cost proxies (ZCPs) provide fast training-free signals, but each proxy usually captures only a partial property of an architecture, leading to unstable rankings across datasets and search spaces. In contrast, GNN-based predictors can improve accuracy, but often depend on labeled architectures, graph encoders, and costly representation learning, which makes them less suitable for extreme few-shot settings. We propose FENAS, a topology-agnostic few-shot prediction framework that turns complementary initialization priors into robust ranking signals. Its core component, CAST, is a feature pipeline that combines copula alignment and synergy transformation. CAST maps heterogeneous ZCPs into a compact feature manifold, enabling lightweight meta-learners to produce reliable rankings from scarce supervision. On NAS-Bench-201, with only 0.5% labeled samples, FENAS achieves a Kendall's Tau (τ) of 0.70, outperforming representative topology-based few-shot predictors in this low-label regime. In closed-domain search, FENAS evaluates the NAS-Bench-201 space in approximately 1138 seconds and achieves competitive test accuracy. By providing a fast, reliable, and structurally simple evaluation mechanism, FENAS supports the design of automated neural architectures under limited evaluation budgets.