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.
2023
Zhao, Junbo; Ning, Xuefei; Liu, Enshu; Ru, Binxin; Zhou, Zixuan; Zhao, Tianchen; Chen, Chen; Zhang, Jiajin; Liao, Qingmin; Wang, Yu
Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS “Cold-Start” Technical Report
2023.
@techreport{Zhao22,
title = {Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS “Cold-Start”},
author = {Junbo Zhao and Xuefei Ning and Enshu Liu and Binxin Ru and Zixuan Zhou and Tianchen Zhao and Chen Chen and Jiajin Zhang and Qingmin Liao and Yu Wang},
url = {https://nicsefc.ee.tsinghua.edu.cn/nics_file/pdf/4208e529-772e-4977-be31-0b7cc4c7a9fc.pdf},
year = {2023},
date = {2023-12-20},
urldate = {2023-12-20},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Zhang, Baochang; Xu, Sheng; Lin, Mingbao; Wang, Tiancheng; Doermann, David
Binary Neural Networks: Algorithms, Architectures, and Applications Book
2023.
@book{ZhangBNN23a,
title = {Binary Neural Networks: Algorithms, Architectures, and Applications },
author = {Baochang Zhang and Sheng Xu and Mingbao Lin and Tiancheng Wang and David Doermann},
url = {https://www.taylorfrancis.com/books/mono/10.1201/9781003376132/binary-neural-networks-baochang-zhang-sheng-xu-mingbao-lin-tiancheng-wang-david-doermann},
year = {2023},
date = {2023-12-13},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Barbudo, Rafael; Ventura, Sebastián; Romero, José Raúl
Eight years of AutoML: categorisation, review and trends Journal Article
In: Knowledge and Information Systems, 2023.
@article{Barbudo-kis23a,
title = {Eight years of AutoML: categorisation, review and trends},
author = {
Rafael Barbudo and Sebastián Ventura and José Raúl Romero
},
url = {https://link.springer.com/article/10.1007/s10115-023-01935-1},
year = {2023},
date = {2023-08-08},
urldate = {2023-08-08},
journal = {Knowledge and Information Systems},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lourens, Matt; Sinayskiy, Ilya; Park, Daniel K.; Blank, Carsten; Petruccione, Francesco
Hierarchical quantum circuit representations for neural architecture search Journal Article
In: npj Quantum Information , 2023.
@article{Lourens-npjqi23a,
title = {Hierarchical quantum circuit representations for neural architecture search},
author = {
Matt Lourens and Ilya Sinayskiy and Daniel K. Park and Carsten Blank and Francesco Petruccione
},
url = {https://www.nature.com/articles/s41534-023-00747-z},
year = {2023},
date = {2023-08-05},
urldate = {2023-08-05},
journal = {npj Quantum Information },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ang, Sui Paul; Phung, Son Lam; Duong, Soan T. M.; Bouzerdoum, Abdesselam
MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection Bachelor Thesis
2023.
@bachelorthesis{Ang-AppliedIntelligence23a,
title = {MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection},
author = {
Sui Paul Ang and Son Lam Phung and Soan T. M. Duong and Abdesselam Bouzerdoum
},
url = {https://link.springer.com/article/10.1007/s10489-023-04682-6},
year = {2023},
date = {2023-08-02},
urldate = {2023-08-02},
journal = {Applied Intelligence },
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
Lukasik, Jovita
Topology Learning for Prediction, Generation, and Robustness in Neural Architecture Search PhD Thesis
2023.
@phdthesis{Lukasik-PHD23a,
title = {Topology Learning for Prediction, Generation, and Robustness in Neural Architecture Search},
author = {Jovita Lukasik},
url = {https://madoc.bib.uni-mannheim.de/64915/1/dissertation_Lukasik.pdf},
year = {2023},
date = {2023-08-01},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Li, Lintao; Jiang, Hongkai; Wang, Ruixin; Yang, Qiao
A reinforcement neural architecture search convolutional neural network for rolling bearing fault diagnosis Journal Article
In: Measurement Science and Technology, vol. 34, no. 11, pp. 115122, 2023.
@article{Li_2023,
title = {A reinforcement neural architecture search convolutional neural network for rolling bearing fault diagnosis},
author = {Lintao Li and Hongkai Jiang and Ruixin Wang and Qiao Yang},
url = {https://dx.doi.org/10.1088/1361-6501/acec06},
doi = {10.1088/1361-6501/acec06},
year = {2023},
date = {2023-08-01},
urldate = {2023-08-01},
journal = {Measurement Science and Technology},
volume = {34},
number = {11},
pages = {115122},
publisher = {IOP Publishing},
abstract = {The complexity of machinery makes accurate identification of rolling bearing fault signals difficult. Convolutional neural networks (CNNs) have made some progress, but they rely on the expertise of the network designer and the iterative process of optimizing numerous parameters. Therefore, there is an urgent need to develop a method that reduces the threshold for designing CNNs for a given task. In this article, we propose a reinforcement neural architecture search CNN to address this problem. Firstly, we design a neural architecture search algorithm that can generate different types of sub-networks specifically for fault diagnosis tasks. Secondly, we execute a reinforcement learning-based search strategy to discover promising sub-networks. Furthermore, we enhance the performance of the sub-network by improving the optimizer and training parameters. We conduct extensive experiments using two different types of datasets and verify that the proposed method’s fault classification capability is superior to existing methods.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wu, Yixiao; Liu, Huan; Zhang, Dalin; Zhang, Yuzhe; Lou, Tianyu; Zheng, Qinghua
AutoEER: automatic EEG-based emotion recognition with neural architecture search Journal Article
In: Journal of Neural Engineering, vol. 20, no. 4, pp. 046029, 2023.
@article{Wu_2023,
title = {AutoEER: automatic EEG-based emotion recognition with neural architecture search},
author = {Yixiao Wu and Huan Liu and Dalin Zhang and Yuzhe Zhang and Tianyu Lou and Qinghua Zheng},
url = {https://dx.doi.org/10.1088/1741-2552/aced22},
doi = {10.1088/1741-2552/aced22},
year = {2023},
date = {2023-08-01},
urldate = {2023-08-01},
journal = {Journal of Neural Engineering},
volume = {20},
number = {4},
pages = {046029},
publisher = {IOP Publishing},
abstract = {Objective. Emotion recognition based on electroencephalography (EEG) is garnering increasing attention among researchers due to its wide-ranging applications and the rise of portable devices. Deep learning-based models have demonstrated impressive progress in EEG-based emotion recognition, thanks to their exceptional feature extraction capabilities. However, the manual design of deep networks is time-consuming and labour-intensive. Moreover, the inherent variability of EEG signals necessitates extensive customization of models, exacerbating these challenges. Neural architecture search (NAS) methods can alleviate the need for excessive manual involvement by automatically discovering the optimal network structure for EEG-based emotion recognition. Approach. In this regard, we propose AutoEER (Automatic EEG-based Emotion Recognition), a framework that leverages tailored NAS to automatically discover the optimal network structure for EEG-based emotion recognition. We carefully design a customized search space specifically for EEG signals, incorporating operators that effectively capture both temporal and spatial properties of EEG. Additionally, we employ a novel parameterization strategy to derive the optimal network structure from the proposed search space. Main results. Extensive experimentation on emotion classification tasks using two benchmark datasets, DEAP and SEED, has demonstrated that AutoEER outperforms state-of-the-art manual deep and NAS models. Specifically, compared to the optimal model WangNAS on the accuracy (ACC) metric, AutoEER improves its average accuracy on all datasets by 0.93%. Similarly, compared to the optimal model LiNAS on the F1 Ssore (F1) metric, AutoEER improves its average F1 score on all datasets by 4.51%. Furthermore, the architectures generated by AutoEER exhibit superior transferability compared to alternative methods. Significance. AutoEER represents a novel approach to EEG analysis, utilizing a specialized search space to design models tailored to individual subjects. This approach significantly reduces the labour and time costs associated with manual model construction in EEG research, holding great promise for advancing the field and streamlining research practices.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rampavan, Medipelly; Ijjina, Earnest Paul
MNASreID: Grasshopper Optimization based Neural Architecture Search for Motorcycle re-IDentification Technical Report
2023.
@techreport{Rampavan-,
title = {MNASreID: Grasshopper Optimization based Neural Architecture Search for Motorcycle re-IDentification},
author = {Medipelly Rampavan and Earnest Paul Ijjina},
url = {https://assets.researchsquare.com/files/rs-3214105/v1_covered_058d7fba-ea8d-4368-ae40-0aad5b9ea1d8.pdf?c=1692415342},
year = {2023},
date = {2023-08-01},
urldate = {2023-08-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Su, Xiu
Adapting Neural Architecture Search for Efficient Deep Learning Models PhD Thesis
2023.
@phdthesis{Su-PHD23a,
title = {Adapting Neural Architecture Search for Efficient Deep Learning Models},
author = {Xiu Su},
url = {https://ses.library.usyd.edu.au/bitstream/handle/2123/31550/thesis_xiu_camera_ready%20(1).pdf?sequence=1},
year = {2023},
date = {2023-08-01},
urldate = {2023-08-01},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
ZiWen, Dou; YuQi, Li; Dong, Ye
FasterMDE: A real-time monocular depth estimation search method that balances accuracy and speed on the edge Journal Article
In: Applied Intelligence, 2023.
@article{Dou-AI23a,
title = {FasterMDE: A real-time monocular depth estimation search method that balances accuracy and speed on the edge},
author = {Dou ZiWen and Li YuQi and Ye Dong
},
url = {https://link.springer.com/article/10.1007/s10489-023-04872-2},
year = {2023},
date = {2023-07-26},
urldate = {2023-07-26},
journal = {Applied Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hosseini, Ramtin; Zhang, Li; Garg, Bhanu; Xie, Pengtao
Fair and Accurate Decision Making through Group-Aware Learning Conference
ICML 2023, 2023.
@conference{HosseiniICML23a,
title = {Fair and Accurate Decision Making through Group-Aware Learning},
author = {Ramtin Hosseini and Li Zhang and Bhanu Garg and Pengtao Xie},
url = {https://openreview.net/pdf?id=p2gZYLZVEb},
year = {2023},
date = {2023-07-24},
urldate = {2023-07-24},
booktitle = {ICML 2023},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Wang, Wei; Wang, Xianpeng; Song, Xiangman
Maximal sparse convex surrogate-assisted evolutionary convolutional neural architecture search for image segmentation Journal Article
In: Complex & Intelligent Systems , 2023.
@article{WangCIS23a,
title = {Maximal sparse convex surrogate-assisted evolutionary convolutional neural architecture search for image segmentation},
author = {
Wei Wang and Xianpeng Wang and Xiangman Song
},
url = {https://link.springer.com/article/10.1007/s40747-023-01166-5},
year = {2023},
date = {2023-07-24},
urldate = {2023-07-24},
journal = {Complex & Intelligent Systems },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Yang, Yi; Wei, Jiaxuan; Yu, Zhixuan; Zhang, Ruisheng
A trustworthy neural architecture search framework for pneumonia image classification utilizing blockchain technology Journal Article
In: The Journal of Supercomputing, 2023.
@article{Yang-js23a,
title = {A trustworthy neural architecture search framework for pneumonia image classification utilizing blockchain technology},
author = {Yi Yang and Jiaxuan Wei and Zhixuan Yu and Ruisheng Zhang},
url = {https://link.springer.com/article/10.1007/s11227-023-05541-4},
year = {2023},
date = {2023-07-20},
urldate = {2023-07-20},
journal = {The Journal of Supercomputing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Liu, Ruihua; Nan, Haoyu; Zou, Yangyang; Xie, Ting
AS-3DFCN: Automatically Seeking 3DFCN-Based Brain Tumor Segmentation Conference
Cognitive Computation, 2023.
@conference{Liu-CC23a,
title = {AS-3DFCN: Automatically Seeking 3DFCN-Based Brain Tumor Segmentation},
author = {
Ruihua Liu and Haoyu Nan and Yangyang Zou and Ting Xie
},
url = {https://link.springer.com/article/10.1007/s12559-023-10168-x},
year = {2023},
date = {2023-07-18},
urldate = {2023-07-18},
booktitle = {Cognitive Computation},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Thomson, Sarah L; Ochoa, Gabriela; Veerapen, Nadarajen; Michalak, Krzysztof
Channel Configuration for Neural Architecture: Insights from the Search Space Proceedings Article
In: The Genetic and Evolutionary Computation Conference (GECCO) 2023, 2023.
@inproceedings{Thomson-gecco23,
title = {Channel Configuration for Neural Architecture: Insights from the Search Space},
author = {Thomson, Sarah L and Ochoa, Gabriela and Veerapen, Nadarajen and Michalak, Krzysztof},
url = {https://dspace.stir.ac.uk/handle/1893/34997},
year = {2023},
date = {2023-07-15},
urldate = {2023-07-15},
booktitle = {The Genetic and Evolutionary Computation Conference (GECCO) 2023},
journal = {The Genetic and Evolutionary Computation Conference (GECCO) 2023},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zhang, Anqi; Zhao, Shengmei
Evolutionary-based searching method for quantum circuit architecture Journal Article
In: Quantum Information Processing , 2023.
@article{ZhangQI23a,
title = {Evolutionary-based searching method for quantum circuit architecture},
author = { Anqi Zhang and Shengmei Zhao
},
url = {https://link.springer.com/article/10.1007/s11128-023-04033-x},
year = {2023},
date = {2023-07-14},
urldate = {2023-07-14},
journal = {Quantum Information Processing },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ren, Yaodanjun; Chen, Chen; Qi, Zhengwei
HaeNAS: Hardware-Aware Efficient Neural Architecture Search via Zero-Cost Proxy Miscellaneous
2023.
@misc{Ren-seke23,
title = {HaeNAS: Hardware-Aware Efficient Neural Architecture Search via Zero-Cost Proxy},
author = {Yaodanjun Ren and Chen Chen and Zhengwei Qi},
url = {https://ksiresearch.org/seke/seke23paper/paper116.pdf},
year = {2023},
date = {2023-07-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
(Ed.)
SpeedLimit: Neural Architecture Search for Quantized Transformer Models Collection
2023.
@collection{Chai-eES-FoM23a,
title = {SpeedLimit: Neural Architecture Search for Quantized Transformer Models},
author = {Yuji Chai and Luke Bailey and Yunho Jin and Matthew Karle ans Glenn G. Ko and David Brooks andGu-Yeon Wei and H. T. Kung},
url = {https://openreview.net/pdf?id=RZnYYl9s7o},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-01},
booktitle = {ES-FoMo Workshop at ICML 2023},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
(Ed.)
QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms Collection
2023.
@collection{WuICML23a,
title = {QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms},
author = {Wenjie Wu and Ge Yan and Xudong Lu and Kaisen Pan and Junchi Yan },
url = {https://openreview.net/forum?id=jGYxcXSg8C},
year = {2023},
date = {2023-06-21},
urldate = {2023-06-21},
booktitle = {ICML 2023},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
(Ed.)
Data Driven Designing of Convolutional Neural Networks Architectures for Image Classification. Collection
2023.
@collection{KawaEL23a,
title = {Data Driven Designing of Convolutional Neural Networks Architectures for Image Classification.},
author = {Kawa, Sajad Ahmad and Wani, M. Arif},
url = {https://web.p.ebscohost.com/abstract?site=ehost&scope=site&jrnl=1816093X&AN=164069087&h=IxpEBMHVyxqZ5rCOBxGR%2f48yb%2fhXK2gflxS6Xw%2fgMeBoeZzeeDHisgLOtCjuD3LPKhjxKJiYsXJnksUUZrz%2bxQ%3d%3d&crl=c&resultLocal=ErrCrlNoResults&resultNs=Ehost&crlhashurl=login.aspx%3fdirect%3dtrue%26profile%3dehost%26scope%3dsite%26authtype%3dcrawler%26jrnl%3d1816093X%26AN%3d164069087},
year = {2023},
date = {2023-06-19},
urldate = {2023-06-19},
booktitle = {Engineering Letters},
journal = {Engineering Letters},
volume = {31},
issue = {2},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
Wang, Runqi; Yang, Linlin; Chen, Hanlin; Wang, Wei; Doermann, David; Zhang, Baochang
Anti-Bandit for Neural Architecture Search Journal Article
In: International Journal of Computer Vision , 2023.
@article{nokey,
title = {Anti-Bandit for Neural Architecture Search},
author = {
Runqi Wang and Linlin Yang and Hanlin Chen and Wei Wang and David Doermann and Baochang Zhang
},
url = {https://link.springer.com/article/10.1007/s11263-023-01826-6},
year = {2023},
date = {2023-06-17},
urldate = {2023-06-17},
journal = {International Journal of Computer Vision },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Feng, Yen-Wei; Jiang, Bing-Ru; Lin, Albert
NeuroEvolution-based network architecture evolution in semicon- ductor manufacturing Technical Report
2023.
@techreport{FengRG23,
title = {NeuroEvolution-based network architecture evolution in semicon- ductor manufacturing},
author = {Yen-Wei Feng and Bing-Ru Jiang and Albert Lin},
url = {https://www.researchgate.net/publication/371448999_NeuroEvolution-based_network_architecture_evolution_in_semiconductor_manufacturing},
year = {2023},
date = {2023-06-10},
urldate = {2023-06-10},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
(Ed.)
Rethinking Dilated Convolution for Real-time Semantic Segmentation Collection
2023.
@collection{Gao-CVPR23a,
title = {Rethinking Dilated Convolution for Real-time Semantic Segmentation},
author = {Roland Gao},
url = {https://openaccess.thecvf.com/content/CVPR2023W/ECV/papers/Gao_Rethinking_Dilated_Convolution_for_Real-Time_Semantic_Segmentation_CVPRW_2023_paper.pdf},
year = {2023},
date = {2023-06-02},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
Wang, Yu; Drgoňa, Ján; Zhang, Jiaxin; Suryanarayana, Karthik Somayaji Nanjangud; Schram, Malachi; Liu, Frank; Li, Peng
AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 8, pp. 10244-10252, 2023.
@article{Wang_Drgoňa_Zhang_NanjangudSuryanarayana_Schram_Liu_Li_2023,
title = {AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution},
author = {Yu Wang and Ján Drgoňa and Jiaxin Zhang and Karthik Somayaji Nanjangud Suryanarayana and Malachi Schram and Frank Liu and Peng Li},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/26220},
doi = {10.1609/aaai.v37i8.26220},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {37},
number = {8},
pages = {10244-10252},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lu, Shun; Hu, Yu; Wang, Peihao; Han, Yan; Tan, Jianchao; Li, Jixiang; Yang, Sen; Liu, Ji
PINAT: A Permutation INvariance Augmented Transformer for NAS Predictor Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 7, pp. 8957-8965, 2023.
@article{Lu_Hu_Wang_Han_Tan_Li_Yang_Liu_2023,
title = {PINAT: A Permutation INvariance Augmented Transformer for NAS Predictor},
author = {Shun Lu and Yu Hu and Peihao Wang and Yan Han and Jianchao Tan and Jixiang Li and Sen Yang and Ji Liu},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/26076},
doi = {10.1609/aaai.v37i7.26076},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {37},
number = {7},
pages = {8957-8965},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ma, Dongning; Zhao, Pengfei; Jiao, Xun
PerfHD: Efficient ViT Architecture Performance Ranking Using Hyperdimensional Computing Proceedings Article
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 2229-2236, 2023.
@inproceedings{Ma_2023_CVPR,
title = {PerfHD: Efficient ViT Architecture Performance Ranking Using Hyperdimensional Computing},
author = {Dongning Ma and Pengfei Zhao and Xun Jiao},
url = {https://openaccess.thecvf.com/content/CVPR2023W/NAS/html/Ma_PerfHD_Efficient_ViT_Architecture_Performance_Ranking_Using_Hyperdimensional_Computing_CVPRW_2023_paper.html},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
pages = {2229-2236},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hendrickx, Lotte; Symons, Arne; Ranst, Wiebe Van; Verhelst, Marian; Goedemé, Toon
Hardware-Aware NAS by Genetic Optimisation With a Design Space Exploration Simulator Proceedings Article
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 2274-2282, 2023.
@inproceedings{Hendrickx_2023_CVPR,
title = {Hardware-Aware NAS by Genetic Optimisation With a Design Space Exploration Simulator},
author = {Lotte Hendrickx and Arne Symons and Wiebe Van Ranst and Marian Verhelst and Toon Goedemé},
url = {https://openaccess.thecvf.com/content/CVPR2023W/NAS/html/Hendrickx_Hardware-Aware_NAS_by_Genetic_Optimisation_With_a_Design_Space_Exploration_CVPRW_2023_paper.html},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
pages = {2274-2282},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
-, Kwang
On Impact of Network Architecture for Deep Learning PhD Thesis
2023.
@phdthesis{Fu-phd23a,
title = {On Impact of Network Architecture for Deep Learning},
author = {Kwang -},
url = {https://www.proquest.com/openview/4846714308fa452ca2ed972e3c254d7d/1?pq-origsite=gscholar&cbl=18750&diss=y},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Pijackova, Kristyna; Nejedly, Petr; Kremen, Vaclav; Plesinger, Filip; Mivalt, Filip; Lepkova, Kamila; Pail, Martin; Jurak, Pavel; Worrell, Gregory; Brazdil, Milan; Klimes, Petr
Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis Journal Article
In: Journal of Neural Engineering, vol. 20, no. 3, pp. 036034, 2023.
@article{Pijackova_2023,
title = {Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis},
author = {Kristyna Pijackova and Petr Nejedly and Vaclav Kremen and Filip Plesinger and Filip Mivalt and Kamila Lepkova and Martin Pail and Pavel Jurak and Gregory Worrell and Milan Brazdil and Petr Klimes},
url = {https://dx.doi.org/10.1088/1741-2552/acdc54},
doi = {10.1088/1741-2552/acdc54},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {Journal of Neural Engineering},
volume = {20},
number = {3},
pages = {036034},
publisher = {IOP Publishing},
abstract = {Objective. The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process, we propose an automatic method, a novel approach to enhance the optimization of neural network architectures for processing intracranial electroencephalogram (iEEG) data. Approach. We present a genetic algorithm, which optimizes neural network architecture and signal pre-processing parameters for iEEG classification. Main results. Our method improved the macro F1 score of the state-of-the-art model in two independent datasets, from St. Anne’s University Hospital (Brno, Czech Republic) and Mayo Clinic (Rochester, MN, USA), from 0.9076 to 0.9673 and from 0.9222 to 0.9400 respectively. Significance. By incorporating principles of evolutionary optimization, our approach reduces the reliance on human intuition and empirical guesswork in architecture design, thus promoting more efficient and effective neural network models. The proposed method achieved significantly improved results when compared to the state-of-the-art benchmark model (McNemar’s test, p ≪ 0.01). The results indicate that neural network architectures designed through machine-based optimization outperform those crafted using the subjective heuristic approach of a human expert. Furthermore, we show that well-designed data preprocessing significantly affects the models’ performance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Theodorakopoulos, Daphne; Manss, Christoph; Stahl, Frederic; Lindauer, Marius
GREEN-AUTOML FOR PLASTIC LITTER DETECTION Conference
ICLR 2023 workshop, Tackling Climate Change with Machine Learning, 2023.
@conference{TheodorakopoulosTCCML23a,
title = {GREEN-AUTOML FOR PLASTIC LITTER DETECTION},
author = {Daphne Theodorakopoulos and Christoph Manss and Frederic Stahl and Marius Lindauer},
url = {https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/iclr2023/53/paper.pdf},
year = {2023},
date = {2023-05-31},
urldate = {2023-05-31},
booktitle = {ICLR 2023 workshop, Tackling Climate Change with Machine Learning},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
P, Pushpaveni H; Yadav, Anil Kumar; Yadav, Santosh Kumar; Adhikari, Sulav Narayan
Detection of Skin Cancer using Neural Architecture Search with Model Quantization Journal Article
In: International Journal of Advances in Engineering Architecture Science and Technology, vol. 1, iss. 2, 2023.
@article{PushpaveniIJADAST23a,
title = {Detection of Skin Cancer using Neural Architecture Search with Model Quantization},
author = {Pushpaveni H P and Anil Kumar Yadav and Santosh Kumar Yadav and Sulav Narayan Adhikari},
url = {https://www.ijaeast.com/IJAEAST_0006052023.pdf},
year = {2023},
date = {2023-05-31},
urldate = {2023-05-31},
journal = {International Journal of Advances in Engineering Architecture Science and Technology},
volume = {1},
issue = {2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Deng, Shaojiang; Fan, Chao; Li, Yantao; Qin, Huafeng; El-Yacoubi, Mounim A.; Zhou, Gang
GRU-based Neural Architecture Search for Finger-Vein Identification Technical Report
2023.
@techreport{Deng-icccn23a,
title = {GRU-based Neural Architecture Search for Finger-Vein Identification},
author = {Shaojiang Deng and Chao Fan and Yantao Li and Huafeng Qin and Mounim A. El-Yacoubi and Gang Zhou},
url = {http://wmit-pages-prod.s3.amazonaws.com/wp-content/uploads/sites/13/2023/05/08123125/ICCCN23.pdf},
year = {2023},
date = {2023-05-26},
urldate = {2023-05-26},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
(Ed.)
AstroNet: When Astrocyte Meets Artificial Neural Network Collection
2023.
@collection{Han-CVPR23a,
title = {AstroNet: When Astrocyte Meets Artificial Neural Network},
author = {Mengqiao Han and Liyuan Pan and Xiabi Liu},
url = {https://openaccess.thecvf.com/content/CVPR2023/papers/Han_AstroNet_When_Astrocyte_Meets_Artificial_Neural_Network_CVPR_2023_paper.pdf},
year = {2023},
date = {2023-05-25},
urldate = {2023-05-25},
booktitle = {CVPR2023},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
(Ed.)
DisWOT: Student Architecture Search for Distillation WithOut Training Collection
2023.
@collection{Dong-cvpr23a,
title = {DisWOT: Student Architecture Search for Distillation WithOut Training},
author = {Peijie Dong and Lunjun Li and Zimian Wei},
url = {https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_DisWOT_Student_Architecture_Search_for_Distillation_WithOut_Training_CVPR_2023_paper.pdf},
year = {2023},
date = {2023-05-25},
booktitle = {CVPR2023},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
(Ed.)
MDL-NAS: A Joint Multi-domain Learning Framework for Vision Transformer Collection
2023.
@collection{Wang-CVPR23a,
title = {MDL-NAS: A Joint Multi-domain Learning Framework for Vision Transformer},
author = {Shiguang Wang and Tao Xie and Jian Cheng and Xingcheng Zhang and Haijun Liu},
url = {https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MDL-NAS_A_Joint_Multi-Domain_Learning_Framework_for_Vision_Transformer_CVPR_2023_paper.pdf},
year = {2023},
date = {2023-05-25},
urldate = {2023-05-25},
booktitle = {CVPR2023},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
Thuan, Nguyen Duc; Dong, Trinh Phuong; Nguyen, Hue Thi; Hoang, Hong Si
Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature Journal Article
In: Engineering Research Express, vol. 5, no. 2, 2023.
@article{Thuan-ERE23a,
title = {Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature},
author = {Nguyen Duc Thuan and Trinh Phuong Dong and Hue Thi Nguyen and Hong Si Hoang},
url = {https://iopscience.iop.org/article/10.1088/2631-8695/acd625/meta},
year = {2023},
date = {2023-05-25},
urldate = {2023-05-25},
journal = { Engineering Research Express},
volume = {5},
number = {2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Huang, Junhao; Xue, Bing; Sun, Yanan; Zhang, Mengjie; Yen, Gary G
Split-Level Evolutionary Neural Architecture Search With Elite Weight Inheritance. Journal Article
In: IEEE transactions on neural networks and learning systems, 2023.
@article{Huang-tnnls23,
title = {Split-Level Evolutionary Neural Architecture Search With Elite Weight Inheritance.},
author = { Huang, Junhao and Xue, Bing and Sun, Yanan and Zhang, Mengjie and Yen, Gary G},
url = {https://pubmed.ncbi.nlm.nih.gov/37224355/},
year = {2023},
date = {2023-05-24},
urldate = {2023-05-24},
journal = {IEEE transactions on neural networks and learning systems},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Li, Yifan; Liu, Jing
A survey: evolutionary deep learning Journal Article
In: Soft Computing , 2023.
@article{Li-SC23a,
title = {A survey: evolutionary deep learning},
author = {Yifan Li and Jing Liu},
url = {https://link.springer.com/article/10.1007/s00500-023-08316-4},
year = {2023},
date = {2023-05-23},
urldate = {2023-05-23},
journal = {Soft Computing },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gao, Jianliang; Al-Sabri, Raeed; Oloulade, Babatounde Moctard; Chen, Jiamin; Lyu, Tengfei; Wu, Zhenpeng
GM2NAS: multitask multiview graph neural architecture search Journal Article
In: Knowledge and Information Systems , 2023.
@article{GaoKIS23,
title = {GM2NAS: multitask multiview graph neural architecture search},
author = {
Jianliang Gao and Raeed Al-Sabri and Babatounde Moctard Oloulade and Jiamin Chen and Tengfei Lyu and Zhenpeng Wu
},
url = {https://link.springer.com/article/10.1007/s10115-023-01886-7},
year = {2023},
date = {2023-05-15},
urldate = {2023-05-15},
journal = {Knowledge and Information Systems },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Loni, Mohammad; Mohan, Aditya; Asadi, Mehdi; Lindauer, Marius
Learning Activation Functions for Sparse Neural Networks Technical Report
2023.
@techreport{loni2023learning,
title = {Learning Activation Functions for Sparse Neural Networks},
author = {Mohammad Loni and Aditya Mohan and Mehdi Asadi and Marius Lindauer},
url = {https://arxiv.org/abs/2305.10964},
year = {2023},
date = {2023-05-15},
urldate = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
(Ed.)
Searching sharing relationship for instance segmentation decoder Collection
2023.
@collection{Xi-appliedintelligence23,
title = {Searching sharing relationship for instance segmentation decoder},
author = {Yuling Xi and Ning Wang and Shaohua Wan and Xiaoming Wang and Peng Wang and Yanning Zhang
},
url = {https://link.springer.com/article/10.1007/s10489-022-04434-y},
year = {2023},
date = {2023-05-02},
urldate = {2023-05-02},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
Yang, Tiejun; He, Qing; Huang, Lin
OM-NAS: pigmented skin lesion image classification based on a neural architecture search Journal Article
In: Biomed. Opt. Express, vol. 14, no. 5, pp. 2153–2165, 2023.
@article{Yang:23,
title = {OM-NAS: pigmented skin lesion image classification based on a neural architecture search},
author = {Tiejun Yang and Qing He and Lin Huang},
url = {https://opg.optica.org/boe/abstract.cfm?URI=boe-14-5-2153},
doi = {10.1364/BOE.483828},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
journal = {Biomed. Opt. Express},
volume = {14},
number = {5},
pages = {2153--2165},
publisher = {Optica Publishing Group},
abstract = {Because pigmented skin lesion image classification based on manually designed convolutional neural networks (CNNs) requires abundant experience in neural network design and considerable parameter tuning, we proposed the macro operation mutation-based neural architecture search (OM-NAS) approach in order to automatically build a CNN for image classification of pigmented skin lesions. We first used an improved search space that was oriented toward cells and contained micro and macro operations. The macro operations include InceptionV1, Fire and other well-designed neural network modules. During the search process, an evolutionary algorithm based on macro operation mutation was employed to iteratively change the operation type and connection mode of parent cells so that the macro operation was inserted into the child cell similar to the injection of virus into host DNA. Ultimately, the searched best cells were stacked to build a CNN for the image classification of pigmented skin lesions, which was then assessed on the HAM10000 and ISIC2017 datasets. The test results showed that the CNN built with this approach was more accurate than or almost as accurate as state-of-the-art (SOTA) approaches such as AmoebaNet, InceptionV3 + Attention and ARL-CNN in terms of image classification. The average sensitivity of this method on the HAM10000 and ISIC2017 datasets was 72.4% and 58.5%, respectively.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ouertatani, Houssem; Maxim, Cristian; Niar, Smail; Talbi, El-Ghazali
Bayesian optimization for NAS with pretrained deep ensembles Proceedings Article
In: International Conference in Optimization and Learning (OLA),, 2023.
@inproceedings{Ouertatani-OLA23,
title = { Bayesian optimization for NAS with pretrained deep ensembles},
author = {Houssem Ouertatani and Cristian Maxim and Smail Niar and El-Ghazali Talbi},
url = {https://hal.science/hal-04076075/},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
booktitle = {International Conference in Optimization and Learning (OLA),},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Yao, Dixi
Solving Heterogeneity via Personalized Federated Learning Technical Report
2023.
@techreport{YaoMISC23a,
title = {Solving Heterogeneity via Personalized Federated Learning},
author = {Dixi Yao},
url = {https://dixiyao.github.io/assets/papers/1771.pdf},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Li, Yantao; Luo, Jiaxing; Deng, Shaojiang; Zhou, Gang
SearchAuth: Neural Architecture Search Based Continuous Authentication Using Auto Augmentation Search Journal Article
In: ACM Trans. Sen. Netw., 2023, ISSN: 1550-4859, (Just Accepted).
@article{10.1145/3599727,
title = {SearchAuth: Neural Architecture Search Based Continuous Authentication Using Auto Augmentation Search},
author = {Yantao Li and Jiaxing Luo and Shaojiang Deng and Gang Zhou},
url = {https://doi.org/10.1145/3599727},
doi = {10.1145/3599727},
issn = {1550-4859},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
journal = {ACM Trans. Sen. Netw.},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Mobile devices have been playing significant roles in our daily lives, which has made device security and privacy protection extremely important. These mobile devices storing user sensitive and private information, therefore, need rigorous user authentication mechanisms. In this paper, we present SearchAuth, a novel continuous authentication system on smartphones exploiting a neural architecture search (NAS) to find an optimal network architecture and an auto augmentation search (AAS) to more effectively train the optimal network along with the best data augmentation policies, by leveraging the accelerometer, gyroscope and magnetometer on smartphones to capture users’ behavioral patterns. Specifically, SearchAuth consists of three stages, i.e. the offline stage, registration stage, and authentication stage. In the offline stage, we utilize the NAS on sensor data of the accelerometer, gyroscope and magnetometer to find an optimal network architecture based on the designed search space. With the optimal network architecture, namely NAS-based model, the AAS automatically optimizes the augmentation of the input data for more effectively training the model that is for feature extraction. In the registration stage, we use the trained NAS-based model to learn and extract deep features from the legitimate user’s data, and train the LOF classifier with 55 features selected by the PCA. In the authentication stage, with the well-trained NAS-based model and LOF classifier, SearchAuth identifies the current user as a legitimate user or an impostor when the user starts operating a smartphone. Based on our dataset, we evaluate the performance of the proposed SearchAuth, and the experimental results demonstrate that SearchAuth surpasses the representative authentication schemes by achieving the best accuracy of 93.95%, F1-score of 94.30%, and EER of 5.30% on the LOF classifier with dataset size of 100.},
note = {Just Accepted},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zhang, Xindong
Towards lightweight and efficient network design for image super-resolution PhD Thesis
2023.
@phdthesis{Zhang-phD23,
title = {Towards lightweight and efficient network design for image super-resolution},
author = {Zhang, Xindong},
url = {https://theses.lib.polyu.edu.hk/handle/200/12305},
year = {2023},
date = {2023-04-24},
urldate = {2023-04-24},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Zhang, Zhenman; Xue, Yu; Slowik, Adam; Yuan, Ziming
Sle-CNN: a novel convolutional neural network for sleep stage classification Proceedings Article
In: 2023.
@inproceedings{ZhangNCA23,
title = {Sle-CNN: a novel convolutional neural network for sleep stage classification},
author = {
Zhenman Zhang and Yu Xue and Adam Slowik and Ziming Yuan
},
url = {https://link.springer.com/article/10.1007/s00521-023-08598-7},
year = {2023},
date = {2023-04-23},
urldate = {2023-04-23},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Tragoudaras, Antonios; Antoniadis, Charalampos; Massoud, Yehia
TinyML for EEG Decoding on Microcontrollers Technical Report
2023.
@techreport{Tragoudaras-RG23a,
title = {TinyML for EEG Decoding on Microcontrollers},
author = {Antonios Tragoudaras and Charalampos Antoniadis and Yehia Massoud},
url = {https://www.researchgate.net/profile/Antonios-Tragoudaras/publication/370024910_TinyML_for_EEG_Decoding_on_Microcontrollers/links/64399a6ee881690c4bd535fe/TinyML-for-EEG-Decoding-on-Microcontrollers.pdf},
year = {2023},
date = {2023-04-21},
urldate = {2023-04-21},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Chatzimichailidis, Avraam
ALTERNATIVE OPTIMIZATION METHODS FOR TRAINING OF LARGE DEEP NEURAL NETWORKS PhD Thesis
2023.
@phdthesis{Chatzimichailidis-PhD23,
title = {ALTERNATIVE OPTIMIZATION METHODS FOR TRAINING OF LARGE DEEP NEURAL NETWORKS},
author = {Avraam Chatzimichailidis},
url = {https://kluedo.ub.rptu.de/frontdoor/deliver/index/docId/7241/file/PhD_Thesis_Avraam_Chatzimichailidis.pdf},
year = {2023},
date = {2023-04-19},
urldate = {2023-04-19},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}