AutoML.org

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.

3849 entries « ‹ 2 of 77 › »

2024

3799.

Lovison-Franco, Bruno; Miquel, Jonathan; Romdhane, Aymen; Prenat, Guillaume; Anghel, Lorena; Novo, David; Benoit, Pascal

Trade-offs in Neural Network Compression: Quantized and Binary Models for Keyword Spotting Proceedings Article

In: ICECS 2024 - 31st IEEE International Conference on Electronics Circuits and Systems, pp. In press, Nancy, France, 2024.

3798.

Kolodochka, Dmytro; Polyakova, Marina; Nesteriuk, Oleksandr; Makarichev, Victor

LaMa network architecture search for image inpainting Journal Article

In: ICST-2024: Information Control Systems & Technologies, , 2024.

3797.

D'souza, Melwin; Gurpur, Ananth Prabhu; Kumara, Varuna

SANAS-Net: spatial attention neural architecture search for breast cancer detection Journal Article

In: IAES International Journal of Artificial Intelligence (IJ-AI), vol. 13, no. 3, 2024, ISBN: 2252-8938.

3796.

Zhang, Shutao; Chen, Qian

Deep Learning Approach for CSI Feedback in Massive MIMO Technical Report

2024.

3795.

Bargagna, Filippo; Zigrino, Donato; Santi, Lisa Anita De; Genovesi, Dario; Scipioni, Michele; Favilli, Brunella; Vergaro, Giuseppe; Emdin, Michele; Giorgetti, Assuero; Positano, Vincenzo; Santarelli, Maria Filomena

Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images Journal Article

In: 2024.

3794.

Selg, Hardi; Shibin, Konstantin; Tsertov, Anton; Jenihhin, Maksim; Ellervee, Peeter; Raik, Jaan

Special Session: In-Field ML-Assisted Intermittent Fault Localization and Management in RISC-V SoCs Proceedings Article

In: 2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), pp. 1-6, IEEE Computer Society, Los Alamitos, CA, USA, 2024.

Abstract | Links | BibTeX

3793.

Patel, Kush

AutoML and Automated Data Science by Democratizing AI through End-to-End Automation Journal Article

In: Ijraset Journal For Research in Applied Science and Engineering Technology, 2024.

3792.

Kadway, Chetan; Mukhopadhyay, Shalini; Islam, Syed Mujibul; Choudhury, Abhishek Roy; Dey, Swarnava; Dey, Sounak; Mukherjee, Arijit; Pal, Arpan

Zero-Shot Embedded Neural Architecture Search for On-board Satellite Tasks & Hardware Accelerators Proceedings Article

In: Dold, Dominik; Hadjiivanov, Alexander; Izzo, Dario (Ed.): Proceedings of SPAICE2024: The First Joint European Space Agency / IAA Conference on AI in and for Space, pp. 175-179, 2024.

3791.

Lou, Xiaoxuan

Towards security analysis and design of confidential computing systems PhD Thesis

2024.

3790.

Huang, Lin; Qin, Xi; Yang, Tiejun

MFC-NAS: Multifunctional Cells Based Neural Architecture Search for Plant Images Classification Miscellaneous

2024.

3789.

Cari, Kamal Ud-Din; Mercury, Marin; Alaba, Guiomar

A Snapshot of Tiny AI: Innovations in Model Compression and Deployment Journal Article

In: Innovations in Model Compression and Deployment, 2024.

3788.

Jiao, Qing; Hu, Weifei; Hao, Guangbo; Cheng, Jin; Peng, Xiang; Liu, Zhenyu; Tan, Jianrong

A digital twin of intelligent robotic grasping based on single-loop-optimized differentiable architecture search and sim-real collaborative learning Journal Article

In: Journal of Intelligent Manufacturing , 2024.

3787.

Awarayi, Nicodemus Songose; Twum, Frimpong; yemang, Kwabena Owusu-Ag Kwabena Owusu-Agyemang

A Neural Architecture Search CNN for Alzheimer’s Disease Classification Journal Article

In: ECTI Transactions on CIT, 2024.

3786.

Loroch, Dominik Marek

Hardware-Aware Neural Architecture Search Book Section

In: 2024.

3785.

Li, Guihong

Theoretically-grounded efficient deep learning system design Bachelor Thesis

2024.

3784.

Slimani, Hicham; Mhamdi, Jamal El; Jilbab, Abdelilah

Open-access Deep Learning Structure for Real-time Crop Monitoring Based on Neural Architecture Search and UAV Journal Article

In: Braz. arch. biol. technol., 2024.

3783.

Han, Shihao; Liu, Sishuo; Du, Shucheng; Li, Mingzi; Ye, Zijian; Xu, Xiaoxin; Li, Yi; Wang, Zhongrui; Shang, Dashan

CMN: a co-designed neural architecture search for efficient computing-in-memory-based mixture-of-experts Journal Article

In: Science China Information Sciences, 2024.

3782.

Xiong, Suqin; Li, Yang; Wang, Jun; Zhang, Zhi; Wang, Hao; Lu, Lijun

Anti-islanding image detection and optimization of distributed power supply using neural network architecture search Journal Article

In: Discover Computing , 2024.

3781.

Ma, QuanGong; Hao, ChaoLong; Yang, XuKui; Qian, LongLong; Zhang, Hao; Si, NianWen; Xu, MinChen; Qu, Dan

Continuous evolution for efficient quantum architecture search Journal Article

In: EPJ Quantum Technology , 2024.

3780.

Zhao, ZiHao; Tang, XiangHong; Lu, JianGuang; Huang, Yong

Lightweight graph neural network architecture search based on heuristic algorithms Journal Article

In: International Journal of Machine Learning and Cybernetics , 2024.

3779.

(Ed.)

BALANCING ERROR AND LATENCY OF BLACK-BOX MODELS FOR AUDIO EFFECTS USING HARDWARE-AWARE NEURAL ARCHITECTURE SEARCH Collection

2024.

3778.

Fu, Wei; Lou, Wenqi; Gong, Lei; Wang, Chao; Zhou, Xuehai

Beyond Training: A Zero-Shot Framework to Neural Architecture and Accelerator Co-Exploration Proceedings Article

In: 2024 IEEE International Conference on Cluster Computing Workshops (CLUSTER Workshops), pp. 148-149, IEEE Computer Society, Los Alamitos, CA, USA, 2024.

Abstract | Links | BibTeX

3777.

Krzywda, Maciej; Łukasik, Szymon; H, Amir Gandomi

Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks Technical Report

2024.

3776.

Hao, Zixiang

Advancing Lung Cancer Diagnosis: Federated Learning-Based Privacy Innovations Miscellaneous

2024.

3775.

Wang, Di

Federated Learning Using GPT-4 Boosted Particle Swarm Optimization for Compact Neural Architecture Search Journal Article

In: Journal of Advances in Information Technology, 2024.

3774.

(Ed.)

RepTor: Re-parameterizable Temporal Convolution for Keyword Spotting via Differentiable Kernel Search Collection

2024.

3773.

Dai, Menghang; Liu, Zhiliang; He, Zixiao

Wafer defect pattern recognition based on differentiable architecture search with dual attention module Journal Article

In: Measurement Science and Technology, vol. 35, no. 12, pp. 125102, 2024.

Abstract | Links | BibTeX

3772.

Ouertatani, Houssem; Maxim, Cristian; Niar, Smail; Talbi, El-Ghazali

Accelerated NAS via pretrained ensembles and multi-fidelity Bayesian Optimization Proceedings Article

In: 33rd International Conference on Artificial Neural Networks (ICANN), Lugano, Switzerland, 2024.

3771.

Xiao, Jiamin; Yu, Kuoyong; Zhao, Bo; Liu, Derong

Evolutionary Neural Architecture Search with Performance Predictor Based on Hybrid Encodings Technical Report

2024.

3770.

Ajani, Oladayo S.; Darlan, Daison; Ivan, Dzeuban Fenyom; Mallipeddi, Rammohan

Multi-indicator based multi-objective evolutionary algorithm with application to neural architecture search Journal Article

In: International Journal of Machine Learning and Cybernetics , 2024.

3769.

Moutaouekkil, Mohammed A.; Karkri, Aboulkacem; Koulali, Mohammed A.; Taybi, Chakib; Kahlaoui, Mohammed

Plant Root Characterization Using Ground-Penetrating Radar with Deep Learning Journal Article

In: Arabian Journal for Science and Engineering, 2024.

3768.

Mebirouk, Nadjib; Amrane, Moussa; Messast, Salah; Mazouzi, Smaine

Enhanced analysis of landslide susceptibility mapping in the proximity of main roads in the province of Skikda, Algeria: using NAS for efficient performance and faster processing Journal Article

In: Modeling Earth Systems and Environment , vol. 10, 2024.

3767.

(Ed.)

Medical Neural Architecture Search: Survey and Taxonomy Collection

2024.

3766.

Siti, Prajwal Khadka; Adhikari, Sujata

Optimization Strategies and Neural Architectures in Neural Networks: Dataset Pruning, Architecture Search, and Diffusion Models Journal Article

In: International Journal of Applied Machine Learning and Computational Intelligence, vol. 14, no. 8, pp. 41–55, 2024.

3765.

Zhang, Jinnian; Chen, Weijie; Joshi, Tanmayee; Uyanik, Meltem; Zhang, Xiaomin; Loh, Po-Ling; Jog, Varun; Bruce, Richard; Garrett, John; McMillan, Alan

RobMedNAS: searching robust neural network architectures for medical image synthesis Journal Article

In: Biomedical Physics & Engineering Express, vol. 10, no. 5, pp. 055029, 2024.

Abstract | Links | BibTeX

3764.

Jeevidha, S. Saraswathi S.

Learning Methods and Parameters used in Neural Architecture Search for Image Classification Journal Article

In: International Journal of Computer Applications, vol. 186, no. 32, pp. 19-24, 2024, ISSN: 0975-8887.

3763.

Improved Learning Through Neural Component Search PhD Thesis

2024.

3762.

(Ed.)

A COMPARATIVE STUDY: DEEPFAKE VIDEO DETECTION WITH NASNETS AND LSTM Collection

2024.

3761.

Ge, Wanying

Spoofing-robust Automatic Speaker Verification: Architecture, Explainability and Joint Optimisation PhD Thesis

2024.

3760.

Hosseini, Ramtin

Trustworthy Neural Architecture Search PhD Thesis

2024.

3759.

Zhang, Lei; Zheng, Xiawu; Wu, Jiarui; Chang, Xiang; Copner, Nigel; Lin, Chih-Min; Chao, Fei; Chen, Yanjie; Shang, Changjing; Shen, Qiang

A Hybrid Performance Estimation Strategy for Optimizing Neural Architecture Search Proceedings Article

In: UKCI 2024: 23rd UK Workshop on Computational Intelligence, 2024, (23rd UK Workshop on Computational Intelligence and 8th International Conference on Belief Functions (BELIEF 2024), UKCI 2024 ; Conference date: 04-09-2024 Through 06-09-2024).

Abstract | Links | BibTeX

@inproceedings{a9df99457d0b46eaa30b74322b84a9f0,

title = {A Hybrid Performance Estimation Strategy for Optimizing Neural Architecture Search},

author = {Lei Zhang and Xiawu Zheng and Jiarui Wu and Xiang Chang and Nigel Copner and Chih-Min Lin and Fei Chao and Yanjie Chen and Changjing Shang and Qiang Shen},

url = {https://research.aber.ac.uk/cy/publications/a-hybrid-performance-estimation-strategy-for-optimizing-neural-ar},

year  = {2024},

date = {2024-07-24},

urldate = {2024-07-24},

booktitle = {UKCI 2024: 23rd UK Workshop on Computational Intelligence},

abstract = {The emergence of neural architecture search (NAS) technology has lowered the professional threshold for optimizing model architectures. However, existing NAS methods primarily evaluate performance by fully training a network architecture, which is computationally expensive and slow. This paper proposes a hybrid performance estimation strategy search framework for neural architecture search, which can flexibly adjust the performance evaluation strategy at each stage. In the initial stage, this study uses less accurate but low-cost methods to quickly eliminate suboptimal architectures. As the search progresses, more computationally intensive but accurate evaluation strategies are employed to filter out the optimal network architectures. In the final stage, more precise verification is conducted to ensure that the selected network architecture achieves the best performance in practice. This research can adapt to different precision and speed requirements, providing flexible reduction space ratio strategies aimed at meeting accuracy requirements while maintaining efficiency. Its generalizability and flexibility help address various NAS challenges. Experimental results show that the method proposed in this study performs excellently in multiple benchmark tests, achieving a balance between performance and efficiency. Additionally, by testing on other search spaces, datasets, and tasks, this study demonstrates its good generalization ability.},

note = {23rd UK Workshop on Computational Intelligence and 8th International Conference on Belief Functions (BELIEF 2024), UKCI 2024 ; Conference date: 04-09-2024 Through 06-09-2024},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

3758.

Baniata, Hamza

SoK: quantum computing methods for machine learning optimization Journal Article

In: Quantum Machine Intelligence , vol. 6, 2024.

3757.

Wu, Ruoyou; Li, Cheng; Zou, Juan; Liu, Xinfeng; Zheng, Hairong; Wang, Shanshan

Generalizable Reconstruction for Accelerating MR Imaging via Federated Learning with Neural Architecture Search Journal Article

In: IEEE Trans Med Imaging . , 2024.

3756.

Huang, Chien Yu; Tsai, Cheng-Che; Hwang, Lisa Alice; Kang, Bor-Hwang; Lin, Yaoh-Shiang; Su, Hsing-Hao; Shen, Guan‐Ting; Hsieh, Jun-Wei

SCC-NET: Segmentation of Clinical Cancer image for Head and Neck Squamous Cell Carcinoma Technical Report

2024.

3755.

Asiimwe, Arnold; Das, William; Benmeziane, Hadjer; Maghraoui, Kaoutar El

EfficientMedSAM: Accelerating Medical Image Segmentation via Neural Architecture Search and Knowledge Distillation Conference

EDGE2024, 2024.

3754.

Braatz, Yannick; Soliman, Taha; Rai, Shubham; Rieber, Dennis Sebastian; Bringmann, Oliver

CoNAX: Towards Comprehensive Co-Design Neural Architecture Search Using HW Abstractions Proceedings Article

In: 2024 IEEE 35th International Conference on Application-specific Systems, Architectures and Processors (ASAP), pp. 8-16, IEEE Computer Society, Los Alamitos, CA, USA, 2024.

Abstract | Links | BibTeX

@inproceedings{10631099,

title = { CoNAX: Towards Comprehensive Co-Design Neural Architecture Search Using HW Abstractions },

author = {Yannick Braatz and Taha Soliman and Shubham Rai and Dennis Sebastian Rieber and Oliver Bringmann},

url = {https://doi.ieeecomputersociety.org/10.1109/ASAP61560.2024.00013},

doi = {10.1109/ASAP61560.2024.00013},

year  = {2024},

date = {2024-07-01},

urldate = {2024-07-01},

booktitle = {2024 IEEE 35th International Conference on Application-specific Systems, Architectures and Processors (ASAP)},

pages = {8-16},

publisher = {IEEE Computer Society},

address = {Los Alamitos, CA, USA},

abstract = {HW-aware neural architecture search (HW-NAS) aims to yield high-accuracy neural network (NN) architectures by automatically exploring multiple architectural parameters of potential network candidates. In most HW-NAS approaches, the HW parameter search space is limited. Hence, HW awareness is tied to only a few degrees of design freedom, leading to the following sub-optimalities - First, it restricts exploration of HW parameters, which can potentially lead to better network candidates; Second, HW-NAS is still entirely a software-centric process where HW-awareness is taken care by an HW function exposed to the NAS process and is oblivious to the actual deployment. To tackle the above challenges, this paper proposes a Co-Design Neural Architecture Search (Co-NAS) approach that simultaneously explores hardware and neural architecture variations, thus allowing for full system optimization. By connecting the mutual impact of variable neural networks and HW parameters on the network's prediction accuracy and on-device efficiency in a shared optimization loop, Co-Nas finds designs of optimum performance and enables HW/SW Co-Design. This work aims to enable more diverse HW search spaces (higher degrees of design freedom) for ML accelerators (such as using a virtual prototype) and efficient exploration by integrating abstract ML accelerator and NN architecture modeling into a comprehensive Co-Nas environment. In our experiments, we explore hardware variations of a baseline accelerator architecture to demonstrate how our work can help find designs with better hardware latency and comparable network accuracy. Designs yielded by our framework provide a speedup of $1.4times$ compared to the baseline on a restricted SW search space at the same HW resources.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

3753.

(Ed.)

Are Large Language Models Good Neural Architecture Generators for Edge? Collection

2024.

3752.

Yin, Jia; Wang, Wei; Guo, Zhonghua; Ji, Yangchun

DTS: dynamic training slimming with feature sparsity for efficient convolutional neural network Journal Article

In: Journal of Real-Time Image Processing, 2024.

3751.

(Ed.)

Neural Architecture Search for Adversarial Robustness via Learnable Pruning Collection

2024.

3750.

Yang, Jiechao; Liu, Yong

ETAS: Zero-Shot Transformer Architecture Search via Network Trainability and Expressivity Miscellaneous

2024.

3849 entries « ‹ 2 of 77 › »