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Freiburg-Hannover-Tübingen

Literature on Neural Architecture Search

Maintained by Difan Deng and Marius Lindauer.

The following list considers papers related to neural architecture search. It is by no means complete. If you miss a paper on the list, please let us know.

Please note that although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization. We would therefore like to share some best practices for empirical evaluations of NAS methods, which we believe will facilitate sustained and measurable progress in the field. If you are interested in a teaser, please read our blog post or directly jump to our checklist.

Transformers have gained increasing popularity in different domains. For a comprehensive list of papers focusing on Neural Architecture Search for Transformer-Based spaces, the awesome-transformer-search repo is all you need.

4169 entries « ‹ 2 of 84 › »

2025

4119.

Ju, Moran; Niu, Buniu; Li, Mulin; Mao, Tengkai; Jin, Si-nian

Toward Better Accuracy-Efficiency Trade-Offs for Oriented SAR Ship Object Detection Bachelor Thesis

2025.

4118.

Liu, Guoqing; Qian, Yuhua; Liang, Xinyan; Fu, Pinhan

Core structure-guided multi-modal classification via Monte Carlo Tree Search Journal Article

In: International Journal of Machine Learning and Cybernetics , 2025.

4117.

HERNANDEZ, ESAU ALAIN HERVERT; CAO, YAN; KEHTARNAVAZ, NASSER

Computationally Efficient Neural Architecture Search for Image Denoising Bachelor Thesis

2025.

4116.

Naayini, Prudhvi; Kamatala, Srikanth; Myakala, Praveen Kumar

Transforming Performance Engineering with Generative AI Journal Article

In: Journal of Computer and Communications , vol. 13, no. 3, 2025.

4115.

Xu, Dikai; Cao, Bin

Adaptive Multiobjective Evolutionary Generative Adversarial Network for Metaverse Network Intrusion Detection Journal Article

In: Science Partner Journals, 2025.

4114.

Li, Yuangang; Ma, Rui; Zhang, Qian; Wang, Zeyu; Zong, Linlin; Liu, Xinyue

Neural architecture search using attention enhanced precise path evaluation and efficient forward evolution Journal Article

In: scientific reports , 2025.

4113.

(Ed.)

HPE-DARTS: Hybrid Pruning and Proxy Evaluation in Differentiable Architecture Search Collection

2025.

4112.

(Ed.)

OPTIMIZED LSTM NEURAL NETWORKS VIA NEURAL ARCHITECTURE SEARCH FOR PREDICTING DAMAGE EVOLUTION IN COMPOSITE LAMINATES Collection

2025.

4111.

Fang, Xuwei; Xie, Weisheng; Li, Hui; Zhou, Wenbin; Hang, Chen; Gao, Xiangxiang

DARTS-EAST: an edge-adaptive selection with topology first differentiable architecture selection method Journal Article

In: Applied Intelligence , 2025.

4110.

(Ed.)

Neural Architecture Search: Tradeoff Between Performance and Efficiency Collection

2025.

4109.

(Ed.)

PQNAS: Mixed-precision Quantization-aware Neural Architecture Search with Pseudo Quantizer Collection

2025.

4108.

He, Zhimin; Chen, Hongxiang; Zhou, Yan; Situ, Haozhen; Li, Yongyao; Li, Lvzhou

Self-supervised representation learning for Bayesian quantum architecture search Journal Article

In: Phys. Rev. A, vol. 111, iss. 3, pp. 032403, 2025.

4107.

Feng, Shiyang; Li, Zhaowei; Zhang, Bo; Chen, Tao

DSF2-NAS: Dual-Stage Feature Fusion via Network Architecture Search for Classification of Multimodal Remote Sensing Images Journal Article

In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. , 2025.

4106.

(Ed.)

TinyDevID: TinyML-Driven IoT Devices IDentification Using Network Flow Data Collection

2025.

4105.

Yu, Sixing

Scalable and resource-efcient federated learning: Techniques for resource-constrained heterogeneous systems PhD Thesis

2025.

4104.

Fu, Jintao; Cong, Peng; Xu, Shuo; Chang, Jiahao; Liu, Ximing; Sun, Yuewen

Neural architecture search with Deep Radon Prior for sparse-view CT image reconstruction Journal Article

In: Med Phys , 2025.

4103.

Zhao, Yi-Heng; Pang, Shen-Wen; Huang, Heng-Zhi; Wu, Shao-Wen; Sun, Shao-Hua; Liu, Zhen-Bing; Pan, Zhi-Chao

Automatic clustering of single-molecule break junction data through task-oriented representation learning Journal Article

In: Rare Metals , 2025.

4102.

Huang, Tao

Efficient Deep Neural Architecture Design and Training PhD Thesis

2025.

4101.

Herterich, Nils; Liu, Kai; Stein, Anthony

Multi-objective neural architecture search for real-time weed detection on embedded system Miscellaneous

2025.

4100.

Tabak, Gabriel Couto; Molenaar, Dylan; Curi, Mariana

An evolutionary neural architecture search for item response theory autoencoders Journal Article

In: Behaviormetrika , 2025.

4099.

Hao, Debei; Pei, Songwei

MIG-DARTS: towards effective differentiable architecture search by gradually mitigating the initial-channel gap between search and evaluation Journal Article

In: Neural Computing and Applications, 2025.

4098.

(Ed.)

H4H: Hybrid Convolution-Transformer Architecture Search for NPU-CIM Heterogeneous Systems for AR/VR Applications Collection

2025.

4097.

Hou, Libin; Wang, Linyuan; Hou, Senbao; Liu, Tianyuan; Ma, Shuxiao; Chen, Jian; Yan, Bin

Low redundancy cell-based Neural Architecture Search for large convolutional neural networks Journal Article

In: Neurocomputing, vol. 649, pp. 130644, 2025, ISSN: 0925-2312.

Abstract | Links | BibTeX

4096.

Huang, Zitong; Montazerin, Mansooreh; Srivastava, Ajitesh

SWAT-NN: Simultaneous Weights and Architecture Training for Neural Networks in a Latent Space Technical Report

2025.

4095.

Kim, Jingeun; Yoon, Yourim

Pruning for efficient DenseNet via surrogate-model-assisted genetic algorithm considering neural architecture search proxies Journal Article

In: Swarm and Evolutionary Computation, vol. 97, pp. 101983, 2025, ISSN: 2210-6502.

Abstract | Links | BibTeX

@article{KIM2025101983,

title = {Pruning for efficient DenseNet via surrogate-model-assisted genetic algorithm considering neural architecture search proxies},

author = {Jingeun Kim and Yourim Yoon},

url = {https://www.sciencedirect.com/science/article/pii/S2210650225001415},

doi = {https://doi.org/10.1016/j.swevo.2025.101983},

issn = {2210-6502},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Swarm and Evolutionary Computation},

volume = {97},

pages = {101983},

abstract = {Recently, convolution neural networks have achieved remarkable progress in computer vision. These neural networks have a large number of parameters, which should be limited in resource-constrained environments. To address this problem, new pruning approaches have explored using neural architecture search (NAS) to determine optimal subnetworks. We propose a novel pruning framework using a surrogate model-assisted genetic algorithm considering NAS proxies (SMA-GA-NP). We applied multi-dimensional encoding and designed crossover and mutation methods. To reduce the search time of NAS, we leveraged a surrogate model to approximate the fitness value of individuals and used NAS proxies, such as reducing the number of epochs and the training set size. The DenseNet-BC (k = 12) model was used as the baseline. We achieved highly competitive performance on CIFAR-10 compared with other GA-based pruning methods and baselines. For CIFAR-100, we reduced the number of parameters by 11.25% to 18.75%, while achieving less than 1% performance degradation compared to the baseline model. These findings highlight SMA-GA-NP’s effectiveness in significantly reducing the number of parameters while having a negligible impact on the model’s performance. We also conducted an ablation study to explore the efficiency of the GA settings, the surrogate model, and NAS proxies in SMA-GA-NP and identified the current limitations and future potential of SMA-GA-NP.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

4094.

Zhong, Zhiwei; Liu, Xianming; Jiang, Junjun; Zhao, Debin; Wang, Shiqi

Dual-Level Cross-Modality Neural Architecture Search for Guided Image Super-Resolution Journal Article

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-18, 2025.

4093.

Jing, Haizhao; Zhang, Haokui; Shang, Zhenhao; Xiao, Rong; Wang, Peng; Zhang, Yanning

Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning Technical Report

2025.

4092.

Lv, Zeqiong; Qian, Chao; Liu, Yun; Fan, Jiahao; Sun, Yanan

Runtime Analysis of Evolutionary NAS for Multiclass Classification Technical Report

2025.

4091.

Martyniuk, Darya; Jung, Johannes; Barta, Daniel; Paschke, Adrian

Benchmarking Quantum Architecture Search with Surrogate Assistance Technical Report

2025.

4090.

Gode, Chetan; Nanche, Bhushan Marutirao; Dhabliya, Dharmesh; Shelke, Rahul Dnyanoba; Patil, Rajendra V.; Bhosle, Sushma

Dynamic neural architecture search : A pathway to efficiently optimized deep learning models Journal Article

In: Journal of Information and Optimization Sciences, vol. 46, no. 4-A, pp. 1117–1127, 2025.

4089.

Ji, Han; Feng, Yuqi; Fan, Jiahao; Sun, Yanan

Loss Functions for Predictor-based Neural Architecture Search Technical Report

2025.

4088.

Kazadi, Joël

Exploring Optimal Neural Network Architectures: What benefits does Reinforcement Learning offer? Bachelor Thesis

2025.

4087.

Yang, Yu; Wang, Siqi; Zhang, Gan; Wang, Qifu; Qin, Yao; Zhai, Dandan; Yang, Zhiqing; Li, Peng

GA-OMTL: Genetic algorithm optimization for multi-task neural architecture search in NIR spectroscopy Journal Article

In: Expert Systems with Applications, vol. 290, pp. 128517, 2025, ISSN: 0957-4174.

Abstract | Links | BibTeX

@article{YANG2025128517,

title = {GA-OMTL: Genetic algorithm optimization for multi-task neural architecture search in NIR spectroscopy},

author = {Yu Yang and Siqi Wang and Gan Zhang and Qifu Wang and Yao Qin and Dandan Zhai and Zhiqing Yang and Peng Li},

url = {https://www.sciencedirect.com/science/article/pii/S0957417425021360},

doi = {https://doi.org/10.1016/j.eswa.2025.128517},

issn = {0957-4174},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Expert Systems with Applications},

volume = {290},

pages = {128517},

abstract = {Near-infrared (NIR) spectroscopy analysis based on deep learning has emerged as a powerful tool for the quality assessment of food and agricultural products. However, most existing multi-task deep learning (MTL) network architectures rely on manual design, struggle to efficiently adapt to diverse datasets, and often neglect the interaction of task-specific (private) features across tasks. To address these challenges, this study proposes a genetic algorithm (GA)-optimized MTL model, termed GA-OMTL, which integrates the strengths of neural architecture search and GA for multi-task prediction of spectral data. The model enhances both feature extraction and task-specific feature interaction by incorporating searchable components such as residual modules (Resblock), batch normalization (BN) layers, Squeeze-and-Excitation (SE) modules, and feature interaction modules. The effectiveness of GA-OMTL was validated using two datasets: American ginseng and wheat flour. In the prediction of protopanaxatriol-type ginsenosides (PPT) and protopanaxadiol-type ginsenosides (PPD) in American ginseng, the R2, RMSE, and RPD values achieved by GA-OMTL were 0.93, 0.70 mg/g, and 3.83 (PPT), and 0.98, 2.03 mg/g, and 7.16 (PPD), respectively. For the prediction of protein and moisture content in wheat flour, the R2, RMSE, and RPD values were 0.99, 0.29 mg/g, and 8.22 (protein), and 0.97, 0.22 mg/g, and 5.67 (moisture), respectively. The experimental results demonstrate that GA-OMTL outperforms three comparison methods in prediction accuracy, highlighting its potential for complex spectral tasks and confirming the practicality and robustness of the proposed model.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

4086.

Jha, Abhash Kumar; Moradian, Shakiba; Krishnakumar, Arjun; Rapp, Martin; Hutter, Frank

$textbackslashtextttconfopt$: A Library for Implementation and Evaluation of Gradient-based One-Shot NAS Methods Proceedings Article

In: AutoML 2025 ABCD Track, 2025.

4085.

Zhao, Lingxiao; Zeng, Xianwen

BTSEG-Nas: a neural network architecture search-based multimodal MRI segmentation network for brain tumors Proceedings Article

In: Zhu, Peicheng; Lin, Guihua (Ed.): Fifth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2025), pp. 136441X, International Society for Optics and Photonics SPIE, 2025.

4084.

Huang, Junhao; Xue, Bing; Sun, Yanan; Zhang, Mengjie; Yen, Gary G.

LightMix: Multi-Objective Search for Lightweight Mixed-Scale Convolutional Neural Networks Journal Article

In: IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-15, 2025.

4083.

Hossain, Md Adnan Faisal; Zhu, Fengqing

Structured Pruning and Quantization for Learned Image Compression Technical Report

2025.

4082.

Shen, Ruina; Peng, Zhangheng; Dong, Le; Wang, Jun; Dong, Weisheng; Shi, Guangming

Searching efficient network with lightweight optimization for image super-resolution Journal Article

In: Neurocomputing, vol. 647, pp. 130550, 2025, ISSN: 0925-2312.

Abstract | Links | BibTeX

@article{SHEN2025130550,

title = {Searching efficient network with lightweight optimization for image super-resolution},

author = {Ruina Shen and Zhangheng Peng and Le Dong and Jun Wang and Weisheng Dong and Guangming Shi},

url = {https://www.sciencedirect.com/science/article/pii/S0925231225012226},

doi = {https://doi.org/10.1016/j.neucom.2025.130550},

issn = {0925-2312},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Neurocomputing},

volume = {647},

pages = {130550},

abstract = {Despite rapid advances in deep learning-based image super-resolution (SR), deploying them in resource-constrained scenarios (e.g., mobile devices) has remained a challenging task. Generally speaking, good performance of SR networks comes at the price of increased computational complexity and model parameters. To strike an improved trade-off, recent studies have considered the design of efficient SR networks. However, most existing methods only consider the network with fewer parameters and calculations, overlooking the actual inference latency time of the network on mobile devices. To meet this challenge of supporting real-time applications, a neural architecture search (NAS) method is developed to find an efficient SR network in this paper, where search space is designed, including efficient operations and lightweight attention blocks. Besides, a novel search method for channel numbers is proposed, called filter grouping search. And a subnet sampling-updating method is designed to accelerate the search. Finally, the weight pruning and adaptive weighted uncertainty-driven loss combined with contrastive distillation are used to optimize the final target network. The experimental results show that our method is superior to the existing state-of-the-art methods. Additionally, the proposed method is also tested on mobile devices and found that it has more advantages on the Kirin 990 5G ARM CPU, supporting its potential in mobile computing. The code is available at https://github.com/douzaikongcheng/EMNAS.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

4081.

Ji, Han; Feng, Yuqi; Fan, Jiahao; Sun, Yanan

CARL: Causality-guided Architecture Representation Learning for an Interpretable Performance Predictor Technical Report

2025.

4080.

Yu, Jiaqi; Gao, Yang; Yang, Hong; Tian, Zhihong; Zhang, Peng; Zhu, Xingquan

Automated graph anomaly detection with large language models Journal Article

In: Knowledge-Based Systems, vol. 324, pp. 113809, 2025, ISSN: 0950-7051.

Abstract | Links | BibTeX

4079.

Kumar, Swagat; Zaech, Jan-Nico; Wilmott, Colin Michael; Gool, Luc Van

RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations Technical Report

2025.

4078.

Coil, Collin; Cheney, Nick

What Makes Freezing Layers in Deep Neural Networks Effective? A Linear Separability Perspective Proceedings Article

In: AutoML 2025 Methods Track, 2025.

4077.

Pava, Mateo Avila; Groh, René; Kist, Andreas M

EG-ENAS: Efficient and Generalizable Evolutionary Neural Architecture Search for Image Classification Proceedings Article

In: AutoML 2025 Methods Track, 2025.

4076.

Roshtkhari, Mehraveh Javan; Toews, Matthew; Pedersoli, Marco

Iterative Monte Carlo Tree Search for Neural Architecture Search Proceedings Article

In: AutoML 2025 Methods Track, 2025.

4075.

Chen, Xueqian; Mao, Zhaoyong; Chen, Zhiwei; Shen, Junge

EvoNAS4Battery: An Evolutionary NAS Approach for Predicting the Remaining Useful Life of Lithium-Ion Batteries Journal Article

In: IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-16, 2025.

4074.

Song, Wenhao; Wu, Xuan; Yang, Bo; Zhou, You; Xiao, Yubin; Liang, Yanchun; Ge, Hongwei; Lee, Heow Pueh; Wu, Chunguo

Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution Technical Report

2025.

4073.

Zhou, Yuyang; Neri, Ferrante; Ong, Yew-Soon; Bai, Ruibin

SiamNAS: Siamese Surrogate Model for Dominance Relation Prediction in Multi-objective Neural Architecture Search Technical Report

2025.

4072.

Huang, Hongtao; Chang, Xiaojun; Yao, Lina

Flexiffusion: Training-Free Segment-Wise Neural Architecture Search for Efficient Diffusion Models Technical Report

2025.

4071.

Garavagno, Andrea Mattia; Ragusa, Edoardo; Frisoli, Antonio; Gastaldo, Paolo

Searching Neural Architectures for Sensor Nodes on IoT Gateways Technical Report

2025.

4070.

Yu, Xinyi; Yin, Runan; Lin, Zhihao; Wang, Yongtao

ERF-NAS: Efficient Receptive Field-Based Zero-Shot NAS for Object Detection Proceedings Article

In: Bue, Alessio Del; Canton, Cristian; Pont-Tuset, Jordi; Tommasi, Tatiana (Ed.): Computer Vision – ECCV 2024 Workshops, pp. 219–234, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-91979-4.

Abstract | Links | BibTeX

4169 entries « ‹ 2 of 84 › »