# A Study on Encodings for Neural Architecture Search

arXiv: https://arxiv.org/abs/2007.04965 · HTML: https://mlsanity.com/paper/2007.04965/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 30 of 38)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [How Powerful are Performance Predictors in Neural Architecture Search?](https://mlsanity.com/paper/2104.01177/) (2021)
- [NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy](https://mlsanity.com/paper/2201.13396/) (2022)
- [NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies](https://mlsanity.com/paper/2210.03230/) (2022)
- [CATE: Computation-aware Neural Architecture Encoding with Transformers](https://mlsanity.com/paper/2102.07108/) (2021)
- [NAS-Bench-x11 and the Power of Learning Curves](https://mlsanity.com/paper/2111.03602/) (2021)
- [On Redundancy and Diversity in Cell-based Neural Architecture Search](https://mlsanity.com/paper/2203.08887/) (2022)
- [Learning Where To Look -- Generative NAS is Surprisingly Efficient](https://mlsanity.com/paper/2203.08734/) (2022)
- [EZNAS: Evolving Zero Cost Proxies For Neural Architecture Scoring](https://mlsanity.com/paper/2209.07413/) (2022)
- [Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks](https://mlsanity.com/paper/2409.12255/) (2024)
- [A Survey on Computationally Efficient Neural Architecture Search](https://mlsanity.com/paper/2206.01520/) (2022)
- [Surprisingly Strong Performance Prediction with Neural Graph Features](https://mlsanity.com/paper/2404.16551/) (2024)
- [Encodings for Prediction-based Neural Architecture Search](https://mlsanity.com/paper/2403.02484/) (2024)
- [SeqNAS: Neural Architecture Search for Event Sequence Classification](https://mlsanity.com/paper/2401.03246/) (2024)
- [On Latency Predictors for Neural Architecture Search](https://mlsanity.com/paper/2403.02446/) (2024)
- [Approximate Neural Architecture Search via Operation Distribution Learning](https://mlsanity.com/paper/2111.04670/) (2021)
- [Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search](https://mlsanity.com/paper/2306.02459/) (2023)
- [Graph-based Neural Architecture Search with Operation Embeddings](https://mlsanity.com/paper/2105.04885/) (2021)
- [MAPLE: Microprocessor A Priori for Latency Estimation](https://mlsanity.com/paper/2111.15106/) (2021)
- [On Accelerating Edge AI: Optimizing Resource-Constrained Environments](https://mlsanity.com/paper/2501.15014/) (2025)
- [FEAR: A Simple Lightweight Method to Rank Architectures](https://mlsanity.com/paper/2106.04010/) (2021)
- [Mutation is all you need](https://mlsanity.com/paper/2107.07343/) (2021)
- [B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search](https://mlsanity.com/paper/2202.03005/) (2022)
- [Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering](https://mlsanity.com/paper/2204.14103/) (2022)
- [Tackling Neural Architecture Search With Quality Diversity Optimization](https://mlsanity.com/paper/2208.00204/) (2022)
- [Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles](https://mlsanity.com/paper/2208.09665/) (2022)
- [Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"](https://mlsanity.com/paper/2302.00932/) (2023)
- [Multi-View Encoders for Performance Prediction in LLM-Based Agentic Workflows](https://mlsanity.com/paper/2505.19764/) (2025)
- [Multi-Objective Evolutionary Neural Architecture Search for Recurrent Neural Networks](https://mlsanity.com/paper/2403.11173/) (2024)
- [Global optimization of graph acquisition functions for neural architecture search](https://mlsanity.com/paper/2505.23640/) (2025)

## Abstract (arXiv)

Neural architecture search (NAS) has been extensively studied in the past few years. A popular approach is to represent each neural architecture in the search space as a directed acyclic graph (DAG), and then search over all DAGs by encoding the adjacency matrix and list of operations as a set of hyperparameters. Recent work has demonstrated that even small changes to the way each architecture is encoded can have a significant effect on the performance of NAS algorithms. In this work, we present the first formal study on the effect of architecture encodings for NAS, including a theoretical grounding and an empirical study. First we formally define architecture encodings and give a theoretical characterization on the scalability of the encodings we study Then we identify the main encoding-dependent subroutines which NAS algorithms employ, running experiments to show which encodings work best with each subroutine for many popular algorithms. The experiments act as an ablation study for prior work, disentangling the algorithmic and encoding-based contributions, as well as a guideline for future work. Our results demonstrate that NAS encodings are an important design decision which can have a significant impact on overall performance. Our code is available at https://github.com/naszilla/nas-encodings.