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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.

4564 entries « ‹ 4 of 92 › »

2025

4414.

Randive, Pallavi; Bhagat, Madhuri S.; Bhorkar, Mangesh P.; Bhagat, Rajesh M.; Vinchurkar, Shilpa M.; Shelare, Sagar; Sharma, Shubham; Beemkumar, N.; Hemalatha, S.; Kumar, Parveen; Kedia, Ankit; Massoud, Ehab El Sayed; Gupta, Deepak; Lozanovic, Jasmina

Adaptive optimization of natural coagulants using hybrid machine learning approach for sustainable water treatment Journal Article

In: nature scientific reports , 2025.

4413.

Gupta, Rajat; Jindal, Rakesh

Impact of Too Many Neural Network Layers on Overfitting Journal Article

In: International Journal of Computer Science and Mobile Computing, 2025.

4412.

Rubbani, Syeda

Neuroevolutionary Optimization of Shannon’s Capacity in Edge AI Protocols for M2M Communication Technical Report

2025.

4411.

(Ed.)

CG-CANTS-N: A Versatile Graph-Based Framework for Scalable and Adaptive Problem Solving Across Domains Collection Forthcoming

Forthcoming.

4410.

Boukela, Lynda; Branlard, Julien; Eichler, Annika

Exploring NAS for anomaly detection in superconducting cavities of particle accelerators Journal Article

In: Front. Phys., 2025.

4409.

Athmani, Khaled; Almeasar, Kamal Saleh; Atiki, Elhoussine; Habal, Adel Hassan Yahya; Taallah, Bachir; Guettala, Abdelhamid

Machine learning methods for predicting the durability behavior of earth mortars with date palm ash Journal Article

In: Asian Journal of Civil Engineering , 2025.

4408.

Gopal, Bhavna

Optimizing Deep Neural Networks: Leveraging Structural Characteristics for Enhanced NAS and Adversarial Robustness PhD Thesis

2025.

4407.

alshahrani, Rami Ayied; Khanzada, Tariq Jamil Saifullah

Improved Crime Prediction using Hybrid Neural Architecture Search together with Hyper-parameter Tuning Technical Report

2025.

4406.

Chang, Lei; Rani, Shalli; Akbar, Muhammad Azeem

ChampionNet: a transformer-enhanced neural architecture search framework for athletic performance prediction and training optimization Journal Article

In: Discover Computing , 2025.

4405.

M, V T Ram Pavan Kumar; Shieh, Chin-Shiuh; S, Siva Shankar; Chakrabarti, Prasun

PNASFH-Net: Parallel NAS forward harmonic Network for colon cancer detection using CT images Journal Article

In: Future Technology, vol. 4, no. 2, pp. 76–91, 2025.

4404.

Ranjan, Vivek; Pal, Riya; Saini, Prashant; Nirala, Divyanshu Raj

Neural Architecture Search: Designing Automated AI Models Miscellaneous

2025.

4403.

Melo, Thadeu Pezzin; Andrade, Jefferson Oliveira; Komati, Karin Satie

A Pipeline for Multivariate Time Series Forecasting of Gas Consumption in Pelletization Process Journal Article

In: CLEIej, 2025.

4402.

Meyer-Lee, Gabriel

Towards Practical Automation of Neural Architecture Design PhD Thesis

2025.

4401.

Su, Jun; Tang, Chaolong; Liu, Zhiquan

A Prediction Optimization Method with Federated Learning and Neural Architecture Search for Distributed Renewable Energy Sources Journal Article

In: Distributed Generation &amp; Alternative Energy Journal, vol. 40, no. 02, pp. 213–238, 2025.

4400.

Gong, Tao; Wang, Liang; Ma, Yongjie

Evolutionary neural architecture search based on a modified particle swarm optimization Journal Article

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

4399.

Liu, Xukun; Lv, Haoze; Ma, Fenglong; Wang, Chi; Xu, Dongkuan (DK)

DyESP: Accelerating Hyperparameter-Architecture Search via Dynamic Exploration and Space Pruning Journal Article

In: Proceedings of the AAAI Symposium Series, vol. 5, no. 1, pp. 172-179, 2025.

4398.

Lan, Guojun; Tang, Jian; Chen, Jie; Xing, Jingshu; Zhao, Lijun

An effective dual-predictor controller mechanism using neural architecture search for optimization of residential energy hub system Journal Article

In: Discover Computing , vol. 28, 2025.

4397.

CAPELLO, ALESSIO

An End-to-End Edge-Computing Framework for IoT-enabled Monitoring PhD Thesis

2025.

4396.

Wu, Yue; Huang, Lin; Yang, Tiejun

Breast Ultrasound Image Segmentation Using Multi-branch Skip Connection Search Journal Article

In: Journal of Imaging Informatics in Medicine , 2025.

4395.

Castagnetti, Andrea; Pegatoquet, Alain; Miramond, Benoit; Montfort, Olivier; Huard, Vincent

Hardware-Aware Neural Architecture Search for~Memory constrained Embedded~Neural Networks~Accelerators Proceedings Article

In: 7th IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2025), pp. 5, Bordeaux, France, 2025.

4394.

Toe, Seb Gregory Dal; Tiddeman, Bernard; Zarges, Christine

Evolutionary Neural Architecture Search using Random Weight Distributions Proceedings Article

In: Ochoa, Gabriela (Ed.): Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2025, Málaga, Spain, July 14-18, 2025, Association for Computing Machinery (ACM), United States of America, 2025, (GECCO 2025 : The Genetic and Evolutionary Computation Conference, GECCO ; Conference date: 14-07-2025 Through 18-07-2025).

Abstract | Links | BibTeX

@inproceedings{581d1ec7c5934c3db7cf4e0f5c2b67f1,

title = {Evolutionary Neural Architecture Search using Random Weight Distributions},

author = {Seb Gregory Dal Toe and Bernard Tiddeman and Christine Zarges},

editor = {Gabriela Ochoa},

url = {https://gecco-2025.sigevo.org/HomePage},

doi = {10.1145/3712255.3726664},

year  = {2025},

date = {2025-03-19},

urldate = {2025-03-19},

booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2025, Málaga, Spain, July 14-18, 2025},

publisher = {Association for Computing Machinery (ACM)},

address = {United States of America},

abstract = {We consider the problem of efficiently searching for high-performing neural architectures whilst simultaneously favouring networks of reduced complexity. It is theorised that a complementary set of proxies can be employed for multi-objective optimisation to balance model performance with the size of the network. We demonstrate that a low-cost proxy for the test accuracy of a candidate architecture can be derived from a series of inferences alone. The proxy is paired with a complexity metric based on the number of parameters in the model and the two properties are used in a multi-objective setting. A Pareto Archived Evolutionary Strategy is used to optimise the two objectives simultaneously and deliver a diverse collection of solutions as output. This method is shown to successfully discover low-complexity architectures with minor loss of accuracy as compared to the global optima and does so with statistical reliability. This work offers a proof-of-concept Neural Architecture Search algorithm that removes training from the process entirely. The proposed approach is examined in terms of search behaviour and the complexity reduction that can be achieved by comparing discovered solutions to the top-performing architectures in the search space.},

note = {GECCO 2025 : The Genetic and Evolutionary Computation Conference, GECCO ; Conference date: 14-07-2025 Through 18-07-2025},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

4393.

Tran, Thanh Hai; Nguyen, Dac Tam; Ngo, Minh Duc; Doan, Long; Luong, Ngoc Hoang; Binh, Huynh Thi Thanh

Kernelshap-nas: a shapley additive explanatory approach for characterizing operation influences Journal Article

In: Neural Computing and Applications , 2025.

4392.

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.

4391.

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.

4390.

(Ed.)

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

2025.

4389.

(Ed.)

Neural Architecture Search: Tradeoff Between Performance and Efficiency Collection

2025.

4388.

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.

4387.

(Ed.)

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

2025.

4386.

(Ed.)

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

2025.

4385.

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.

4384.

Xu, Dikai; Cao, Bin

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

In: Science Partner Journals, 2025.

4383.

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.

4382.

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

Computationally Efficient Neural Architecture Search for Image Denoising Bachelor Thesis

2025.

4381.

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.

4380.

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.

4379.

Herterich, Nils; Liu, Kai; Stein, Anthony

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

2025.

4378.

Huang, Tao

Efficient Deep Neural Architecture Design and Training PhD Thesis

2025.

4377.

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.

4376.

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.

4375.

Yu, Sixing

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

2025.

4374.

(Ed.)

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

2025.

4373.

Peri, Gianluca; Chicchi, Lorenzo; Fanelli, Duccio; Giambagli, Lorenzo

SPectral ARchiteCture Search for neural network models Journal Article

In: npj artificial intelligence, 2025.

4372.

Liu, Weijun; Chen, Haoming; Zhan, Leifang; Wu, Jiaxin; Shen, Zhou

Adaptive Bottleneck Architecture Search for Resource-Constrained Continual Learning Technical Report

2025.

4371.

Singh, Karan; Kajal, Kumari; Negi, Sarita

Experimental Analysis of Lightweight CNNs for Real-Time Object Detection on Low-Power Devices Journal Article

In: Journal of Scientific Innovation and Advanced Research (JSIAR) , 2025.

4370.

Zhao, Yao; Lu, Wei; Zhang, Yinghui

Optimizing low-rank decomposition for efficient attention-based vision models via adaptive neural architecture search Bachelor Thesis

2025.

4369.

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

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

In: Behaviormetrika , 2025.

4368.

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.

4367.

(Ed.)

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

2025.

4366.

Laiadi, Djemoui; Athmani, Khaled; Laiadi, Chaker; Hasseine, Abdelmalek; Merzougui, Abdelkrim; Temam, Elhachmi Guettaf

Modeling and Experimental Study of Liquid–liquid Extraction of Water + Formic Acid + 1-Octanol with NaCl and KCl Using Non-random Two-liquid and Artificial Neural Network Models Bachelor Thesis

2025.

4365.

Öcal, Göktuğ; Özgövde, Atay

Network-aware federated neural architecture search Journal Article

In: Future Generation Computer Systems, vol. 162, pp. 107475, 2025, ISSN: 0167-739X.

Abstract | Links | BibTeX

@article{OCAL2025107475,

title = {Network-aware federated neural architecture search},

author = {Göktuğ Öcal and Atay Özgövde},

url = {https://www.sciencedirect.com/science/article/pii/S0167739X24004205},

doi = {https://doi.org/10.1016/j.future.2024.07.053},

issn = {0167-739X},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Future Generation Computer Systems},

volume = {162},

pages = {107475},

abstract = {The cooperation between Deep Learning (DL) and edge devices has further advanced technological developments, allowing smart devices to serve as both data sources and endpoints for DL-powered applications. However, the success of DL relies on optimal Deep Neural Network (DNN) architectures, and manually developing such systems requires extensive expertise and time. Neural Architecture Search (NAS) has emerged to automate the search for the best-performing neural architectures. Meanwhile, Federated Learning (FL) addresses data privacy concerns by enabling collaborative model development without exchanging the private data of clients. In a FL system, network limitations can lead to biased model training, slower convergence, and increased communication overhead. On the other hand, traditional DNN architecture design, emphasizing validation accuracy, often overlooks computational efficiency and size constraints of edge devices. This research aims to develop a comprehensive framework that effectively balances trade-offs between model performance, communication efficiency, and the incorporation of FL into an iterative NAS algorithm. This framework aims to overcome challenges by addressing the specific requirements of FL, optimizing DNNs through NAS, and ensuring computational efficiency while considering the network constraints of edge devices. To address these challenges, we introduce Network-Aware Federated Neural Architecture Search (NAFNAS), an open-source federated neural network pruning framework with network emulation support. Through comprehensive testing, we demonstrate the feasibility of our approach, efficiently reducing DNN size and mitigating communication challenges. Additionally, we propose Network and Distribution Aware Client Grouping (NetDAG), a novel client grouping algorithm tailored for FL with diverse DNN architectures, considerably enhancing efficiency of communication rounds and update balance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

4564 entries « ‹ 4 of 92 › »