# A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS

arXiv: https://arxiv.org/abs/2004.01899 · HTML: https://mlsanity.com/paper/2004.01899/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2020.

## Built on by (most cited first, 30 of 55)

- [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)
- [OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.08860/) (2023)
- [Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning](https://mlsanity.com/paper/2102.03214/) (2021)
- [Stronger NAS with Weaker Predictors](https://mlsanity.com/paper/2102.10490/) (2021)
- [CATE: Computation-aware Neural Architecture Encoding with Transformers](https://mlsanity.com/paper/2102.07108/) (2021)
- [GradSign: Model Performance Inference with Theoretical Insights](https://mlsanity.com/paper/2110.08616/) (2021)
- [DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models](https://mlsanity.com/paper/2305.16943/) (2023)
- [A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models](https://mlsanity.com/paper/2312.07243/) (2023)
- [NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction](https://mlsanity.com/paper/2211.08024/) (2022)
- [RankNAS: Efficient Neural Architecture Search by Pairwise Ranking](https://mlsanity.com/paper/2109.07383/) (2021)
- [A Survey on Computationally Efficient Neural Architecture Search](https://mlsanity.com/paper/2206.01520/) (2022)
- [FLASH: Fast Neural Architecture Search with Hardware Optimization](https://mlsanity.com/paper/2108.00568/) (2021)
- [Regression Language Models for Code](https://mlsanity.com/paper/2509.26476/) (2025)
- [AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing](https://mlsanity.com/paper/2305.10459/) (2023)
- [FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer](https://mlsanity.com/paper/2403.12821/) (2024)
- [RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving](https://mlsanity.com/paper/2108.08019/) (2021)
- [Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search](https://mlsanity.com/paper/2204.05941/) (2022)
- [GP-NAS-ensemble: a model for NAS Performance Prediction](https://mlsanity.com/paper/2301.09231/) (2023)
- [NAR-Former V2: Rethinking Transformer for Universal Neural Network Representation Learning](https://mlsanity.com/paper/2306.10792/) (2023)
- [Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better](https://mlsanity.com/paper/2404.02241/) (2024)
- [3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification](https://mlsanity.com/paper/2101.04287/) (2021)
- [Graph-based Neural Architecture Search with Operation Embeddings](https://mlsanity.com/paper/2105.04885/) (2021)
- [CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS](https://mlsanity.com/paper/2207.07868/) (2022)
- [CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework](https://mlsanity.com/paper/2212.03965/) (2022)
- [ParZC: Parametric Zero-Cost Proxies for Efficient NAS](https://mlsanity.com/paper/2402.02105/) (2024)
- [CAP: A Context-Aware Neural Predictor for NAS](https://mlsanity.com/paper/2406.02056/) (2024)
- [Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation](https://mlsanity.com/paper/2412.15554/) (2024)
- [Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation](https://mlsanity.com/paper/2110.05242/) (2021)
- [Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures](https://mlsanity.com/paper/2207.05321/) (2022)

## Abstract (arXiv)

This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search. Specifically, different from existing graph-based schemes, GATES models the operations as the transformation of the propagating information, which mimics the actual data processing of neural architecture. GATES is a more reasonable modeling of the neural architectures, and can encode architectures from both the "operation on node" and "operation on edge" cell search spaces consistently. Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor. Furthermore, equipped with the improved performance predictor, the sample efficiency of the predictor-based neural architecture search (NAS) flow is boosted. Codes are available at https://github.com/walkerning/aw_nas.