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.
5555
3849.
Zhu, Huijuan; Xia, Mengzhen; Wang, Liangmin; Xu, Zhicheng; Sheng, Victor S.
A Novel Knowledge Search Structure for Android Malware Detection Journal Article
In: IEEE Transactions on Services Computing, no. 01, pp. 1-14, 5555, ISSN: 1939-1374.
@article{10750332,
title = { A Novel Knowledge Search Structure for Android Malware Detection },
author = {Huijuan Zhu and Mengzhen Xia and Liangmin Wang and Zhicheng Xu and Victor S. Sheng},
url = {https://doi.ieeecomputersociety.org/10.1109/TSC.2024.3496333},
doi = {10.1109/TSC.2024.3496333},
issn = {1939-1374},
year = {5555},
date = {5555-11-01},
urldate = {5555-11-01},
journal = {IEEE Transactions on Services Computing},
number = {01},
pages = {1-14},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {While the Android platform is gaining explosive popularity, the number of malicious software (malware) is also increasing sharply. Thus, numerous malware detection schemes based on deep learning have been proposed. However, they are usually suffering from the cumbersome models with complex architectures and tremendous parameters. They usually require heavy computation power support, which seriously limit their deployment on actual application environments with limited resources (e.g., mobile edge devices). To surmount this challenge, we propose a novel Knowledge Distillation (KD) structure—Knowledge Search (KS). KS exploits Neural Architecture Search (NAS) to adaptively bridge the capability gap between teacher and student networks in KD by introducing a parallelized student-wise search approach. In addition, we carefully analyze the characteristics of malware and locate three cost-effective types of features closely related to malicious attacks, namely, Application Programming Interfaces (APIs), permissions and vulnerable components, to characterize Android Applications (Apps). Therefore, based on typical samples collected in recent years, we refine features while exploiting the natural relationship between them, and construct corresponding datasets. Massive experiments are conducted to investigate the effectiveness and sustainability of KS on these datasets. Our experimental results show that the proposed method yields an accuracy of 97.89% to detect Android malware, which performs better than state-of-the-art solutions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
While the Android platform is gaining explosive popularity, the number of malicious software (malware) is also increasing sharply. Thus, numerous malware detection schemes based on deep learning have been proposed. However, they are usually suffering from the cumbersome models with complex architectures and tremendous parameters. They usually require heavy computation power support, which seriously limit their deployment on actual application environments with limited resources (e.g., mobile edge devices). To surmount this challenge, we propose a novel Knowledge Distillation (KD) structure—Knowledge Search (KS). KS exploits Neural Architecture Search (NAS) to adaptively bridge the capability gap between teacher and student networks in KD by introducing a parallelized student-wise search approach. In addition, we carefully analyze the characteristics of malware and locate three cost-effective types of features closely related to malicious attacks, namely, Application Programming Interfaces (APIs), permissions and vulnerable components, to characterize Android Applications (Apps). Therefore, based on typical samples collected in recent years, we refine features while exploiting the natural relationship between them, and construct corresponding datasets. Massive experiments are conducted to investigate the effectiveness and sustainability of KS on these datasets. Our experimental results show that the proposed method yields an accuracy of 97.89% to detect Android malware, which performs better than state-of-the-art solutions.
3848.
Zhang, Feifei; Li, Mao; Ge, Jidong; Tang, Fenghui; Zhang, Sheng; Wu, Jie; Luo, Bin
Privacy-Preserving Federated Neural Architecture Search With Enhanced Robustness for Edge Computing Journal Article
In: IEEE Transactions on Mobile Computing, no. 01, pp. 1-18, 5555, ISSN: 1558-0660.
@article{10742476,
title = { Privacy-Preserving Federated Neural Architecture Search With Enhanced Robustness for Edge Computing },
author = {Feifei Zhang and Mao Li and Jidong Ge and Fenghui Tang and Sheng Zhang and Jie Wu and Bin Luo},
url = {https://doi.ieeecomputersociety.org/10.1109/TMC.2024.3490835},
doi = {10.1109/TMC.2024.3490835},
issn = {1558-0660},
year = {5555},
date = {5555-11-01},
urldate = {5555-11-01},
journal = {IEEE Transactions on Mobile Computing},
number = {01},
pages = {1-18},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {With the development of large-scale artificial intelligence services, edge devices are becoming essential providers of data and computing power. However, these edge devices are not immune to malicious attacks. Federated learning (FL), while protecting privacy of decentralized data through secure aggregation, struggles to trace adversaries and lacks optimization for heterogeneity. We discover that FL augmented with Differentiable Architecture Search (DARTS) can improve resilience against backdoor attacks while compatible with secure aggregation. Based on this, we propose a federated neural architecture search (NAS) framwork named SLNAS. The architecture of SLNAS is built on three pivotal components: a server-side search space generation method that employs an evolutionary algorithm with dual encodings, a federated NAS process based on DARTS, and client-side architecture tuning that utilizes Gumbel softmax combined with knowledge distillation. To validate robustness, we adapt a framework that includes backdoor attacks based on trigger optimization, data poisoning, and model poisoning, targeting both model weights and architecture parameters. Extensive experiments demonstrate that SLNAS not only effectively counters advanced backdoor attacks but also handles heterogeneity, outperforming defense baselines across a wide range of backdoor attack scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With the development of large-scale artificial intelligence services, edge devices are becoming essential providers of data and computing power. However, these edge devices are not immune to malicious attacks. Federated learning (FL), while protecting privacy of decentralized data through secure aggregation, struggles to trace adversaries and lacks optimization for heterogeneity. We discover that FL augmented with Differentiable Architecture Search (DARTS) can improve resilience against backdoor attacks while compatible with secure aggregation. Based on this, we propose a federated neural architecture search (NAS) framwork named SLNAS. The architecture of SLNAS is built on three pivotal components: a server-side search space generation method that employs an evolutionary algorithm with dual encodings, a federated NAS process based on DARTS, and client-side architecture tuning that utilizes Gumbel softmax combined with knowledge distillation. To validate robustness, we adapt a framework that includes backdoor attacks based on trigger optimization, data poisoning, and model poisoning, targeting both model weights and architecture parameters. Extensive experiments demonstrate that SLNAS not only effectively counters advanced backdoor attacks but also handles heterogeneity, outperforming defense baselines across a wide range of backdoor attack scenarios.
3847.
Zhang, Yu-Ming; Hsieh, Jun-Wei; Lee, Chun-Chieh; Fan, Kuo-Chin
RATs-NAS: Redirection of Adjacent Trails on Graph Convolutional Networks for Predictor-based Neural Architecture Search Journal Article
In: IEEE Transactions on Artificial Intelligence, vol. 1, no. 01, pp. 1-11, 5555, ISSN: 2691-4581.
@article{10685480,
title = { RATs-NAS: Redirection of Adjacent Trails on Graph Convolutional Networks for Predictor-based Neural Architecture Search },
author = {Yu-Ming Zhang and Jun-Wei Hsieh and Chun-Chieh Lee and Kuo-Chin Fan},
url = {https://doi.ieeecomputersociety.org/10.1109/TAI.2024.3465433},
doi = {10.1109/TAI.2024.3465433},
issn = {2691-4581},
year = {5555},
date = {5555-09-01},
urldate = {5555-09-01},
journal = {IEEE Transactions on Artificial Intelligence},
volume = {1},
number = {01},
pages = {1-11},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {Manually designed CNN architectures like VGG, ResNet, DenseNet, and MobileNet have achieved high performance across various tasks, but design them is time-consuming and costly. Neural Architecture Search (NAS) automates the discovery of effective CNN architectures, reducing the need for experts. However, evaluating candidate architectures requires significant GPU resources, leading to the use of predictor-based NAS, such as graph convolutional networks (GCN), which is the popular option to construct predictors. However, we discover that, even though the ability of GCN mimics the propagation of features of real architectures, the binary nature of the adjacency matrix limits its effectiveness. To address this, we propose Redirection of Adjacent Trails (RATs), which adaptively learns trail weights within the adjacency matrix. Our RATs-GCN outperform other predictors by dynamically adjusting trail weights after each graph convolution layer. Additionally, the proposed Divide Search Sampling (DSS) strategy, based on the observation of cell-based NAS that architectures with similar FLOPs perform similarly, enhances search efficiency. Our RATs-NAS, which combine RATs-GCN and DSS, shows significant improvements over other predictor-based NAS methods on NASBench-101, NASBench-201, and NASBench-301.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Manually designed CNN architectures like VGG, ResNet, DenseNet, and MobileNet have achieved high performance across various tasks, but design them is time-consuming and costly. Neural Architecture Search (NAS) automates the discovery of effective CNN architectures, reducing the need for experts. However, evaluating candidate architectures requires significant GPU resources, leading to the use of predictor-based NAS, such as graph convolutional networks (GCN), which is the popular option to construct predictors. However, we discover that, even though the ability of GCN mimics the propagation of features of real architectures, the binary nature of the adjacency matrix limits its effectiveness. To address this, we propose Redirection of Adjacent Trails (RATs), which adaptively learns trail weights within the adjacency matrix. Our RATs-GCN outperform other predictors by dynamically adjusting trail weights after each graph convolution layer. Additionally, the proposed Divide Search Sampling (DSS) strategy, based on the observation of cell-based NAS that architectures with similar FLOPs perform similarly, enhances search efficiency. Our RATs-NAS, which combine RATs-GCN and DSS, shows significant improvements over other predictor-based NAS methods on NASBench-101, NASBench-201, and NASBench-301.
3846.
Chen, X.; Yang, C.
CIMNet: Joint Search for Neural Network and Computing-in-Memory Architecture Journal Article
In: IEEE Micro, no. 01, pp. 1-12, 5555, ISSN: 1937-4143.
@article{10551739,
title = {CIMNet: Joint Search for Neural Network and Computing-in-Memory Architecture},
author = {X. Chen and C. Yang},
url = {https://www.computer.org/csdl/magazine/mi/5555/01/10551739/1XyKBmSlmPm},
doi = {10.1109/MM.2024.3409068},
issn = {1937-4143},
year = {5555},
date = {5555-06-01},
urldate = {5555-06-01},
journal = {IEEE Micro},
number = {01},
pages = {1-12},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {Computing-in-memory (CIM) architecture has been proven to effectively transcend the memory wall bottleneck, expanding the potential of low-power and high-throughput applications such as machine learning. Neural architecture search (NAS) designs ML models to meet a variety of accuracy, latency, and energy constraints. However, integrating CIM into NAS presents a major challenge due to additional simulation overhead from the non-ideal characteristics of CIM hardware. This work introduces a quantization and device aware accuracy predictor that jointly scores quantization policy, CIM architecture, and neural network architecture, eliminating the need for time-consuming simulations in the search process. We also propose reducing the search space based on architectural observations, resulting in a well-pruned search space customized for CIM. These allow for efficient exploration of superior combinations in mere CPU minutes. Our methodology yields CIMNet, which consistently improves the trade-off between accuracy and hardware efficiency on benchmarks, providing valuable architectural insights.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Computing-in-memory (CIM) architecture has been proven to effectively transcend the memory wall bottleneck, expanding the potential of low-power and high-throughput applications such as machine learning. Neural architecture search (NAS) designs ML models to meet a variety of accuracy, latency, and energy constraints. However, integrating CIM into NAS presents a major challenge due to additional simulation overhead from the non-ideal characteristics of CIM hardware. This work introduces a quantization and device aware accuracy predictor that jointly scores quantization policy, CIM architecture, and neural network architecture, eliminating the need for time-consuming simulations in the search process. We also propose reducing the search space based on architectural observations, resulting in a well-pruned search space customized for CIM. These allow for efficient exploration of superior combinations in mere CPU minutes. Our methodology yields CIMNet, which consistently improves the trade-off between accuracy and hardware efficiency on benchmarks, providing valuable architectural insights.
3845.
Yan, J.; Liu, J.; Xu, H.; Wang, Z.; Qiao, C.
Peaches: Personalized Federated Learning with Neural Architecture Search in Edge Computing Journal Article
In: IEEE Transactions on Mobile Computing, no. 01, pp. 1-17, 5555, ISSN: 1558-0660.
@article{10460163,
title = {Peaches: Personalized Federated Learning with Neural Architecture Search in Edge Computing},
author = {J. Yan and J. Liu and H. Xu and Z. Wang and C. Qiao},
doi = {10.1109/TMC.2024.3373506},
issn = {1558-0660},
year = {5555},
date = {5555-03-01},
urldate = {5555-03-01},
journal = {IEEE Transactions on Mobile Computing},
number = {01},
pages = {1-17},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {In edge computing (EC), federated learning (FL) enables numerous distributed devices (or workers) to collaboratively train AI models without exposing their local data. Most works of FL adopt a predefined architecture on all participating workers for model training. However, since workers' local data distributions vary heavily in EC, the predefined architecture may not be the optimal choice for every worker. It is also unrealistic to manually design a high-performance architecture for each worker, which requires intense human expertise and effort. In order to tackle this challenge, neural architecture search (NAS) has been applied in FL to automate the architecture design process. Unfortunately, the existing federated NAS frameworks often suffer from the difficulties of system heterogeneity and resource limitation. To remedy this problem, we present a novel framework, termed Peaches, to achieve efficient searching and training in the resource-constrained EC system. Specifically, the local model of each worker is stacked by base cell and personal cell, where the base cell is shared by all workers to capture the common knowledge and the personal cell is customized for each worker to fit the local data. We determine the number of base cells, shared by all workers, according to the bandwidth budget on the parameters server. Besides, to relieve the data and system heterogeneity, we find the optimal number of personal cells for each worker based on its computing capability. In addition, we gradually prune the search space during training to mitigate the resource consumption. We evaluate the performance of Peaches through extensive experiments, and the results show that Peaches can achieve an average accuracy improvement of about 6.29% and up to 3.97× speed up compared with the baselines.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In edge computing (EC), federated learning (FL) enables numerous distributed devices (or workers) to collaboratively train AI models without exposing their local data. Most works of FL adopt a predefined architecture on all participating workers for model training. However, since workers' local data distributions vary heavily in EC, the predefined architecture may not be the optimal choice for every worker. It is also unrealistic to manually design a high-performance architecture for each worker, which requires intense human expertise and effort. In order to tackle this challenge, neural architecture search (NAS) has been applied in FL to automate the architecture design process. Unfortunately, the existing federated NAS frameworks often suffer from the difficulties of system heterogeneity and resource limitation. To remedy this problem, we present a novel framework, termed Peaches, to achieve efficient searching and training in the resource-constrained EC system. Specifically, the local model of each worker is stacked by base cell and personal cell, where the base cell is shared by all workers to capture the common knowledge and the personal cell is customized for each worker to fit the local data. We determine the number of base cells, shared by all workers, according to the bandwidth budget on the parameters server. Besides, to relieve the data and system heterogeneity, we find the optimal number of personal cells for each worker based on its computing capability. In addition, we gradually prune the search space during training to mitigate the resource consumption. We evaluate the performance of Peaches through extensive experiments, and the results show that Peaches can achieve an average accuracy improvement of about 6.29% and up to 3.97× speed up compared with the baselines.
2025
3844.
Wang, Weiqi; Bao, Feilong; Xing, Zhecong; Lian, Zhe
A Survey: Research Progress of Feature Fusion Technology Journal Article
In: 2025.
@article{wangsurvey,
title = {A Survey: Research Progress of Feature Fusion Technology},
author = {Weiqi Wang and Feilong Bao and Zhecong Xing and Zhe Lian},
url = {http://poster-openaccess.com/files/ICIC2024/862.pdf},
year = {2025},
date = {2025-12-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3843.
(Ed.)
MACHINE-GENERATED NEURAL NETWORKS FOR SHORT-TERM LOAD FORECASTING Collection
2025.
@collection{nokey,
title = { MACHINE-GENERATED NEURAL NETWORKS FOR SHORT-TERM LOAD FORECASTING},
author = {Gergana Vacheva and Plamen Stanchev and Nikolay Hinov
},
url = {https://unitechsp.tugab.bg/images/2024/1-EE/s1_p143_v1.pdf},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
booktitle = {International Scientific Conference UNITECH`2024},
journal = {International Scientific Conference UNITECH`2024},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
3842.
Tabak, Gabriel Couto; Molenaar, Dylan; Curi, Mariana
An evolutionary neural architecture search for item response theory autoencoders Journal Article
In: Behaviormetrika , 2025.
@article{nokey,
title = {An evolutionary neural architecture search for item response theory autoencoders},
author = {Gabriel Couto Tabak and Dylan Molenaar and Mariana Curi
},
url = {https://link.springer.com/article/10.1007/s41237-024-00250-5},
year = {2025},
date = {2025-01-27},
urldate = {2025-01-27},
journal = {Behaviormetrika },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3841.
Xu, Jingjing; Wu, Caesar; Li, Yuan-Fang; Danoy, Grégoire; Bouvry, Pascal
A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models Miscellaneous
2025.
@misc{xu2025unifiedhyperparameteroptimizationpipeline,
title = {A Unified Hyperparameter Optimization Pipeline for Transformer-Based Time Series Forecasting Models},
author = {Jingjing Xu and Caesar Wu and Yuan-Fang Li and Grégoire Danoy and Pascal Bouvry},
url = {https://arxiv.org/abs/2501.01394},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
3840.
Capello, Alessio; Berta, Riccardo; Ballout, Hadi; Fresta, Matteo; Soltanmuradov, Vafali; Bellotti, Francesco
Enhancing μNAS for 1D CNNs on Microcontrollers Proceedings Article
In: Valle, Maurizio; Gastaldo, Paolo; Limiti, Ernesto (Ed.): Proceedings of SIE 2024, pp. 481–486, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-71518-1.
@inproceedings{10.1007/978-3-031-71518-1_59,
title = {Enhancing μNAS for 1D CNNs on Microcontrollers},
author = {Alessio Capello and Riccardo Berta and Hadi Ballout and Matteo Fresta and Vafali Soltanmuradov and Francesco Bellotti},
editor = {Maurizio Valle and Paolo Gastaldo and Ernesto Limiti},
url = {https://link.springer.com/chapter/10.1007/978-3-031-71518-1_59},
isbn = {978-3-031-71518-1},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Proceedings of SIE 2024},
pages = {481–486},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Deep Learning (DL) has proved effective in a variety of application domains. However, the computational and memory demand posed by deep neural models limits the spread of DL on resource-constrained devices such as microcontrollers. An opportunity to tailor DL models to specific hardware constraints is given by Neural Architecture Search (NAS), which automatically traverses a large search space, seeking for optimal architectures both in terms of hardware and performance, based on user specifications. State of the art open-source NAS tools for microcontrollers only support 2D Convolutional Neural Network (CNN) and Multi Layer Perceptron (MLP), but do not consider 1D convolution, which is key for time series analysis and signal processing. This study focuses on enhancing the state-of-the-art μNAS framework, by adding support for 1D CNN. Preliminary tests on a dummy dataset consisting of simple gaussian-distributed waveforms, demonstrate the system ability to find appropriate architectures to satisfy the specified constraints.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep Learning (DL) has proved effective in a variety of application domains. However, the computational and memory demand posed by deep neural models limits the spread of DL on resource-constrained devices such as microcontrollers. An opportunity to tailor DL models to specific hardware constraints is given by Neural Architecture Search (NAS), which automatically traverses a large search space, seeking for optimal architectures both in terms of hardware and performance, based on user specifications. State of the art open-source NAS tools for microcontrollers only support 2D Convolutional Neural Network (CNN) and Multi Layer Perceptron (MLP), but do not consider 1D convolution, which is key for time series analysis and signal processing. This study focuses on enhancing the state-of-the-art μNAS framework, by adding support for 1D CNN. Preliminary tests on a dummy dataset consisting of simple gaussian-distributed waveforms, demonstrate the system ability to find appropriate architectures to satisfy the specified constraints.
3839.
Amoury, Sofia El; Smili, Youssef; Fakhri, Youssef
Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization Journal Article
In: Preprints, 2025.
@article{202501.0040,
title = {Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization},
author = {Sofia El Amoury and Youssef Smili and Youssef Fakhri},
url = {https://doi.org/10.20944/preprints202501.0040.v1},
doi = {10.20944/preprints202501.0040.v1},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Preprints},
publisher = {Preprints},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3838.
Su, Yulan; Zhang, Sisi; Lin, Zechao; Wang, Xingbin; Zhao, Lutan; Meng, Dan; Hou, Rui
Poseidon: A NAS-Based Ensemble Defense Method Against Multiple Perturbations Proceedings Article
In: Ide, Ichiro; Kompatsiaris, Ioannis; Xu, Changsheng; Yanai, Keiji; Chu, Wei-Ta; Nitta, Naoko; Riegler, Michael; Yamasaki, Toshihiko (Ed.): MultiMedia Modeling, pp. 215–228, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-2064-7.
@inproceedings{10.1007/978-981-96-2064-7_16,
title = {Poseidon: A NAS-Based Ensemble Defense Method Against Multiple Perturbations},
author = {Yulan Su and Sisi Zhang and Zechao Lin and Xingbin Wang and Lutan Zhao and Dan Meng and Rui Hou},
editor = {Ichiro Ide and Ioannis Kompatsiaris and Changsheng Xu and Keiji Yanai and Wei-Ta Chu and Naoko Nitta and Michael Riegler and Toshihiko Yamasaki},
isbn = {978-981-96-2064-7},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {MultiMedia Modeling},
pages = {215–228},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Deep learning models have been proven to be severely affected by adversarial examples, which limit the widespread deployment of deep learning models. Prior research largely focused on defending against single types of perturbations using a single network. However, these methods are susceptible to sacrificing defense range because defense models that are trained to be robust against one perturbation type may not be robust against other types. Moreover, it is unrealistic to assume that neural networks would only be affected by a single type of perturbation. To defend against multiple perturbations, recent works have attempted to improve the overall robustness against multiple perturbations. Nonetheless, when evaluating the model's robustness against each type of perturbation, multi-perturbation defenses are still significantly less effective than models that are robust against a single perturbation type. To address these issues, we propose Poseidon, an ensemble defense method based on neural architecture search to defend against multiple perturbations. We first highlight the importance of architecture evolution in enhancing model robustness. And a novel robust architecture search method is proposed to identify perturbation-tailored architectures for sub-models. Furthermore, we explore a dedicated ensemble method that can combine these diverse sub-model architectures to be robust against multiple types of perturbations. The experimental results demonstrate that Poseidon outperforms the state-of-the-art multiple perturbation defense methods by 10.9% and 7.4% in robustness on the CIFAR-10 and CIFAR-100 datasets, respectively.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep learning models have been proven to be severely affected by adversarial examples, which limit the widespread deployment of deep learning models. Prior research largely focused on defending against single types of perturbations using a single network. However, these methods are susceptible to sacrificing defense range because defense models that are trained to be robust against one perturbation type may not be robust against other types. Moreover, it is unrealistic to assume that neural networks would only be affected by a single type of perturbation. To defend against multiple perturbations, recent works have attempted to improve the overall robustness against multiple perturbations. Nonetheless, when evaluating the model's robustness against each type of perturbation, multi-perturbation defenses are still significantly less effective than models that are robust against a single perturbation type. To address these issues, we propose Poseidon, an ensemble defense method based on neural architecture search to defend against multiple perturbations. We first highlight the importance of architecture evolution in enhancing model robustness. And a novel robust architecture search method is proposed to identify perturbation-tailored architectures for sub-models. Furthermore, we explore a dedicated ensemble method that can combine these diverse sub-model architectures to be robust against multiple types of perturbations. The experimental results demonstrate that Poseidon outperforms the state-of-the-art multiple perturbation defense methods by 10.9% and 7.4% in robustness on the CIFAR-10 and CIFAR-100 datasets, respectively.
3837.
Zhang, Zhengzhuo; Zhuang, Liansheng
Progressive Neural Architecture Generation with Weaker Predictors Proceedings Article
In: Ide, Ichiro; Kompatsiaris, Ioannis; Xu, Changsheng; Yanai, Keiji; Chu, Wei-Ta; Nitta, Naoko; Riegler, Michael; Yamasaki, Toshihiko (Ed.): MultiMedia Modeling, pp. 229–242, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-2064-7.
@inproceedings{10.1007/978-981-96-2064-7_17,
title = {Progressive Neural Architecture Generation with Weaker Predictors},
author = {Zhengzhuo Zhang and Liansheng Zhuang},
editor = {Ichiro Ide and Ioannis Kompatsiaris and Changsheng Xu and Keiji Yanai and Wei-Ta Chu and Naoko Nitta and Michael Riegler and Toshihiko Yamasaki},
isbn = {978-981-96-2064-7},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {MultiMedia Modeling},
pages = {229–242},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Neural Architecture Search (NAS) methods often suffer from low search efficiency since they have to explore a large and complex architecture search space. To accelerate the architecture search, generative methods learn a search space capturing intricate architecture distributions and generate promising architectures guided by a strong predictor within the latent space. However, since the architecture space is often exponentially large and highly non-convex, even a very strong predictor model is difficult in fitting the whole space, which may degrade their performance. To address this problem, this paper proposes a novel framework named Progressive Neural Architecture Generation with Weaker Predictors (WeakPNAG), which uses conditional diffusion models to generate promising architectures guided by weak predictors. Different from existing generative NAS methods which use a single strong predictor, our WeakPNAG progressively shrinks the sample space based on predictions from previous weak predictors, and updates new weak predictors towards the subspace of better architectures. In this way, our WeakPNAG iteratively learns to generate samples from increasingly promising latent subspaces. Extensive experiments on standard benchmarks demonstrate that our WeakPNAG achieves superior performance with reduced evaluation time compared with SOTA NAS methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Neural Architecture Search (NAS) methods often suffer from low search efficiency since they have to explore a large and complex architecture search space. To accelerate the architecture search, generative methods learn a search space capturing intricate architecture distributions and generate promising architectures guided by a strong predictor within the latent space. However, since the architecture space is often exponentially large and highly non-convex, even a very strong predictor model is difficult in fitting the whole space, which may degrade their performance. To address this problem, this paper proposes a novel framework named Progressive Neural Architecture Generation with Weaker Predictors (WeakPNAG), which uses conditional diffusion models to generate promising architectures guided by weak predictors. Different from existing generative NAS methods which use a single strong predictor, our WeakPNAG progressively shrinks the sample space based on predictions from previous weak predictors, and updates new weak predictors towards the subspace of better architectures. In this way, our WeakPNAG iteratively learns to generate samples from increasingly promising latent subspaces. Extensive experiments on standard benchmarks demonstrate that our WeakPNAG achieves superior performance with reduced evaluation time compared with SOTA NAS methods.
3836.
Sarker, Prodip Kumar
Transformer-based neural architecture search for effective visible-infrared person re-identification Journal Article
In: Neurocomputing, vol. 620, pp. 129257, 2025, ISSN: 0925-2312.
@article{SARKER2025129257,
title = {Transformer-based neural architecture search for effective visible-infrared person re-identification},
author = {Prodip Kumar Sarker},
url = {https://www.sciencedirect.com/science/article/pii/S0925231224020289},
doi = {https://doi.org/10.1016/j.neucom.2024.129257},
issn = {0925-2312},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Neurocomputing},
volume = {620},
pages = {129257},
abstract = {Visible-infrared person re-identification (VI-reID) is a complex task in security and video surveillance that aims to identify and match a person captured by various non-overlapping cameras. In recent years, there has been a notable advancement in reID owing to the development of transformer-based architectures. Although many existing methods emphasize on learning both modality-specific and shared features, challenges remain in fully exploiting the complementary information between infrared and visible modalities. Consequently, there is still opportunity to increase retrieval performance by effectively comprehending and integrating cross-modality semantic information. These designs often have problems with model complexity and time-consuming processes. To tackle these issues, we employ a novel transformer-based neural architecture search (TNAS) deep learning approach for effective VI-reID. To alleviate modality gaps, we first introduce a global–local transformer (GLT) module that captures features at both global and local levels across different modalities, contributing to better feature representation and matching. Then, an efficient neural architecture search (NAS) module is developed to search for the optimal transformer-based architecture, which further enhances the performance of VI-reID. Additionally, we introduce distillation loss and modality discriminative (MD) loss to examine the potential consistency between different modalities to promote intermodality separation between classes and intramodality compactness within classes. Experimental results on two challenging benchmark datasets illustrate that our developed model achieves state-of-the-art results, outperforming existing VI-reID methods.},
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Visible-infrared person re-identification (VI-reID) is a complex task in security and video surveillance that aims to identify and match a person captured by various non-overlapping cameras. In recent years, there has been a notable advancement in reID owing to the development of transformer-based architectures. Although many existing methods emphasize on learning both modality-specific and shared features, challenges remain in fully exploiting the complementary information between infrared and visible modalities. Consequently, there is still opportunity to increase retrieval performance by effectively comprehending and integrating cross-modality semantic information. These designs often have problems with model complexity and time-consuming processes. To tackle these issues, we employ a novel transformer-based neural architecture search (TNAS) deep learning approach for effective VI-reID. To alleviate modality gaps, we first introduce a global–local transformer (GLT) module that captures features at both global and local levels across different modalities, contributing to better feature representation and matching. Then, an efficient neural architecture search (NAS) module is developed to search for the optimal transformer-based architecture, which further enhances the performance of VI-reID. Additionally, we introduce distillation loss and modality discriminative (MD) loss to examine the potential consistency between different modalities to promote intermodality separation between classes and intramodality compactness within classes. Experimental results on two challenging benchmark datasets illustrate that our developed model achieves state-of-the-art results, outperforming existing VI-reID methods.
3835.
Jin, Xiao; Yu, Wen; Chen, Dai-Wei; Shi, Wei
DFD-NAS: General deepfake detection via efficient neural architecture search Journal Article
In: Neurocomputing, vol. 619, pp. 129129, 2025, ISSN: 0925-2312.
@article{JIN2025129129,
title = {DFD-NAS: General deepfake detection via efficient neural architecture search},
author = {Xiao Jin and Wen Yu and Dai-Wei Chen and Wei Shi},
url = {https://www.sciencedirect.com/science/article/pii/S0925231224019003},
doi = {https://doi.org/10.1016/j.neucom.2024.129129},
issn = {0925-2312},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Neurocomputing},
volume = {619},
pages = {129129},
abstract = {As the saying goes, “seeing is believing”. However, with the development of digital face editing tools, we can no longer trust what we can see. Though face forgery detection has made promising progress, most current methods are manually designed by human experts, which is labor-intensive. In this paper, we develop an end-to-end framework based on neural architecture search (NAS) for deepfake detection, which can automatically design network architectures without human intervention. First, a forgery-oriented search space is created to choose appropriate operations for this task, which facilitates the search process in finding the most valuable gradient information for face forgery detection. Second, inspired by the fact that the gap between training error and test error is a good indicator of generalization ability for a classification task, we propose a novel performance estimation metric. This metric encourages the error in the performance estimation phase to be close to the error in the search phase, which guides the search process to select more general models. The cross-dataset search is also considered to develop more general architectures. Eventually, we design a cell cascaded pyramid network (C2PN) for final detection, which aggregates multiscale features for performance improvements. Compared with state-of-the-art networks artificially designed, our method achieves competitive performance in both in-dataset and cross-dataset scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
As the saying goes, “seeing is believing”. However, with the development of digital face editing tools, we can no longer trust what we can see. Though face forgery detection has made promising progress, most current methods are manually designed by human experts, which is labor-intensive. In this paper, we develop an end-to-end framework based on neural architecture search (NAS) for deepfake detection, which can automatically design network architectures without human intervention. First, a forgery-oriented search space is created to choose appropriate operations for this task, which facilitates the search process in finding the most valuable gradient information for face forgery detection. Second, inspired by the fact that the gap between training error and test error is a good indicator of generalization ability for a classification task, we propose a novel performance estimation metric. This metric encourages the error in the performance estimation phase to be close to the error in the search phase, which guides the search process to select more general models. The cross-dataset search is also considered to develop more general architectures. Eventually, we design a cell cascaded pyramid network (C2PN) for final detection, which aggregates multiscale features for performance improvements. Compared with state-of-the-art networks artificially designed, our method achieves competitive performance in both in-dataset and cross-dataset scenarios.
3834.
Li, Jie; Wang, Yuxia; Wang, Yifan; Yu, Ruiyun; Wang, Xingwei
HN-Darts:Hybrid Network Differentiable Architecture Search for Industrial Scenarios Proceedings Article
In: Hadfi, Rafik; Anthony, Patricia; Sharma, Alok; Ito, Takayuki; Bai, Quan (Ed.): PRICAI 2024: Trends in Artificial Intelligence, pp. 322–327, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-0116-5.
@inproceedings{10.1007/978-981-96-0116-5_26,
title = {HN-Darts:Hybrid Network Differentiable Architecture Search for Industrial Scenarios},
author = {Jie Li and Yuxia Wang and Yifan Wang and Ruiyun Yu and Xingwei Wang},
editor = {Rafik Hadfi and Patricia Anthony and Alok Sharma and Takayuki Ito and Quan Bai},
url = {https://link.springer.com/chapter/10.1007/978-981-96-0116-5_26},
isbn = {978-981-96-0116-5},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {PRICAI 2024: Trends in Artificial Intelligence},
pages = {322–327},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Neural architecture search is a powerful tool in image processing, automating model construction and reducing human involvement. However, its deployment on edge devices with limited computing resources is often impeded by the size of large models, a concern overlooked by most NAS methods focused solely on accuracy. We propose a hybrid network search approach that integrates the glore_unit, a novel component that replaces traditional cells to optimize model size without sacrificing accuracy. By leveraging the Differentiable Architecture Search (Darts) and a Googlenet-like hypernet, we've redefined the search space to prioritize compactness and precision, enhanced by a temperature factor for more reliable search selections. Our experiments on cifar10 and ImageNet showcase a model with a 2.35% error rate and 2.76M parameters on cifar10, and a Top-1 error rate of 23.75%, Top-5 error rate of 7.13% with 3.9M parameters on ImageNet, demonstrating SOTA accuracy with a significant reduction in model parameters, making it suitable for environments with constrained computational resources.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Neural architecture search is a powerful tool in image processing, automating model construction and reducing human involvement. However, its deployment on edge devices with limited computing resources is often impeded by the size of large models, a concern overlooked by most NAS methods focused solely on accuracy. We propose a hybrid network search approach that integrates the glore_unit, a novel component that replaces traditional cells to optimize model size without sacrificing accuracy. By leveraging the Differentiable Architecture Search (Darts) and a Googlenet-like hypernet, we've redefined the search space to prioritize compactness and precision, enhanced by a temperature factor for more reliable search selections. Our experiments on cifar10 and ImageNet showcase a model with a 2.35% error rate and 2.76M parameters on cifar10, and a Top-1 error rate of 23.75%, Top-5 error rate of 7.13% with 3.9M parameters on ImageNet, demonstrating SOTA accuracy with a significant reduction in model parameters, making it suitable for environments with constrained computational resources.
3833.
Reddy, P. N. Aravinda; Ramachandra, Raghavendra; Rao, K. Sreenivasa; Mitra, Pabitra
NeuralMultiling: A Novel Neural Architecture Search for Smartphone Based Multilingual Speaker Verification Proceedings Article
In: Antonacopoulos, Apostolos; Chaudhuri, Subhasis; Chellappa, Rama; Liu, Cheng-Lin; Bhattacharya, Saumik; Pal, Umapada (Ed.): Pattern Recognition, pp. 406–423, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-78341-8.
@inproceedings{10.1007/978-3-031-78341-8_26,
title = {NeuralMultiling: A Novel Neural Architecture Search for Smartphone Based Multilingual Speaker Verification},
author = {P. N. Aravinda Reddy and Raghavendra Ramachandra and K. Sreenivasa Rao and Pabitra Mitra},
editor = {Apostolos Antonacopoulos and Subhasis Chaudhuri and Rama Chellappa and Cheng-Lin Liu and Saumik Bhattacharya and Umapada Pal},
url = {https://link.springer.com/chapter/10.1007/978-3-031-78341-8_26},
isbn = {978-3-031-78341-8},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Pattern Recognition},
pages = {406–423},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Multilingual speaker verification introduces the challenge of verifying a speaker in multiple languages. Existing systems were built using i-vector/x-vector approaches along with Bi-LSTMs, which were trained to discriminate speakers, irrespective of the language. Instead of exploring the design space manually, we propose a neural architecture search for multilingual speaker verification suitable for mobile devices, called NeuralMultiling. First, our algorithm searches for an optimal operational combination of neural cells with different architectures for normal cells and reduction cells and then derives a CNN model by stacking neural cells. Using the derived architecture, we performed two different studies:1) language agnostic condition and 2) interoperability between languages and devices on the publicly available Multilingual Audio-Visual Smartphone (MAVS) dataset. The experimental results suggest that the derived architecture significantly outperforms the existing Autospeech method by a 5–6% reduction in the Equal Error Rate (EER) with fewer model parameters.},
keywords = {},
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Multilingual speaker verification introduces the challenge of verifying a speaker in multiple languages. Existing systems were built using i-vector/x-vector approaches along with Bi-LSTMs, which were trained to discriminate speakers, irrespective of the language. Instead of exploring the design space manually, we propose a neural architecture search for multilingual speaker verification suitable for mobile devices, called NeuralMultiling. First, our algorithm searches for an optimal operational combination of neural cells with different architectures for normal cells and reduction cells and then derives a CNN model by stacking neural cells. Using the derived architecture, we performed two different studies:1) language agnostic condition and 2) interoperability between languages and devices on the publicly available Multilingual Audio-Visual Smartphone (MAVS) dataset. The experimental results suggest that the derived architecture significantly outperforms the existing Autospeech method by a 5–6% reduction in the Equal Error Rate (EER) with fewer model parameters.
3832.
Chen, Jiakun; Xu, Jie; Hu, Jiahui; Qiao, Liqiang; Wang, Shuo; Huang, Feiran; Li, Chaozhuo
Context-Aware Structural Adaptive Graph Neural Networks Proceedings Article
In: Hadfi, Rafik; Anthony, Patricia; Sharma, Alok; Ito, Takayuki; Bai, Quan (Ed.): PRICAI 2024: Trends in Artificial Intelligence, pp. 467–479, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-0116-5.
@inproceedings{10.1007/978-981-96-0116-5_39,
title = {Context-Aware Structural Adaptive Graph Neural Networks},
author = {Jiakun Chen and Jie Xu and Jiahui Hu and Liqiang Qiao and Shuo Wang and Feiran Huang and Chaozhuo Li},
editor = {Rafik Hadfi and Patricia Anthony and Alok Sharma and Takayuki Ito and Quan Bai},
url = {https://link.springer.com/chapter/10.1007/978-981-96-0116-5_39},
isbn = {978-981-96-0116-5},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {PRICAI 2024: Trends in Artificial Intelligence},
pages = {467–479},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Graph-based data structures are prevalent in various real-world applications, for example, protein molecules and social connection networks, necessitating effective representation learning techniques. Graph Neural Networks (GNNs) have demonstrated significant advancements in tasks like node classification and social network analysis through recursive information aggregation. However, current GNN approaches are predominantly static, lacking adaptability to specific graph structures. Inspired by Neural Architecture Search (NAS) in designing dataset-specific architectures, we propose Context-Aware Structure Adaptive Graph Neural Networks (CAS-GNN). This framework is capable of automatically selecting the appropriate aggregator for each node which is determined by both node attributes and local contextual information. The selection is formulated as the Markov Decision Process (MDP) optimized via Deep-Q-Network (DQN) training. Our contributions include a flexible framework incorporating various aggregators for individual nodes based on their attributes and local context, improved performance through node-specific aggregator selection, and extensive experimental validation demonstrating the effectiveness of CAS-GNN on multiple real-world datasets.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Graph-based data structures are prevalent in various real-world applications, for example, protein molecules and social connection networks, necessitating effective representation learning techniques. Graph Neural Networks (GNNs) have demonstrated significant advancements in tasks like node classification and social network analysis through recursive information aggregation. However, current GNN approaches are predominantly static, lacking adaptability to specific graph structures. Inspired by Neural Architecture Search (NAS) in designing dataset-specific architectures, we propose Context-Aware Structure Adaptive Graph Neural Networks (CAS-GNN). This framework is capable of automatically selecting the appropriate aggregator for each node which is determined by both node attributes and local contextual information. The selection is formulated as the Markov Decision Process (MDP) optimized via Deep-Q-Network (DQN) training. Our contributions include a flexible framework incorporating various aggregators for individual nodes based on their attributes and local context, improved performance through node-specific aggregator selection, and extensive experimental validation demonstrating the effectiveness of CAS-GNN on multiple real-world datasets.
3831.
Oloulade, Babatounde Moctard; Gao, Jianliang; Chen, Jiamin; Al-Sabri, Raeed; Wu, Zhenpeng; Abdullah, Monir
Shapley-guided pruning for efficient graph neural architecture prediction in distributed learning environments Journal Article
In: Information Sciences, vol. 695, pp. 121695, 2025, ISSN: 0020-0255.
@article{OLOULADE2025121695,
title = {Shapley-guided pruning for efficient graph neural architecture prediction in distributed learning environments},
author = {Babatounde Moctard Oloulade and Jianliang Gao and Jiamin Chen and Raeed Al-Sabri and Zhenpeng Wu and Monir Abdullah},
url = {https://www.sciencedirect.com/science/article/pii/S0020025524016098},
doi = {https://doi.org/10.1016/j.ins.2024.121695},
issn = {0020-0255},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Information Sciences},
volume = {695},
pages = {121695},
abstract = {Graph neural architecture search (GNAS) methods have made significant strides in predictive modeling; however, challenges persist in optimizing these approaches for improved accuracy and scalability. In this paper, we propose GraphNAP++, an innovative framework that combines Shapley-value-guided search space pruning with data-centric distributed learning (DCDL) to address the scalability and efficiency limitations of existing GNAS methodologies. The proposed method begins by selecting a subset of architectures from the search space, which are evaluated on a graph validation dataset using DCDL. These architectures are encoded, and a neural predictor is trained to predict their performance. Shapley values are utilized to prune the search space by retaining only the most influential architectures. The neural predictor then estimates the performance of all architectures within the reduced search space, and the highest-performing architectures are selected for final evaluation. Experimental results on benchmark datasets in distributed learning environments demonstrate the effectiveness of GraphNAP++ in both graph and node classification tasks, highlighting its potential to advance the state-of-the-art for graph neural networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Graph neural architecture search (GNAS) methods have made significant strides in predictive modeling; however, challenges persist in optimizing these approaches for improved accuracy and scalability. In this paper, we propose GraphNAP++, an innovative framework that combines Shapley-value-guided search space pruning with data-centric distributed learning (DCDL) to address the scalability and efficiency limitations of existing GNAS methodologies. The proposed method begins by selecting a subset of architectures from the search space, which are evaluated on a graph validation dataset using DCDL. These architectures are encoded, and a neural predictor is trained to predict their performance. Shapley values are utilized to prune the search space by retaining only the most influential architectures. The neural predictor then estimates the performance of all architectures within the reduced search space, and the highest-performing architectures are selected for final evaluation. Experimental results on benchmark datasets in distributed learning environments demonstrate the effectiveness of GraphNAP++ in both graph and node classification tasks, highlighting its potential to advance the state-of-the-art for graph neural networks.
3830.
Verma, Sahil; Kumar, Prabhat; Singh, Jyoti Prakash
MLP-GNAS: Meta-learning-based predictor-assisted Genetic Neural Architecture Search system Journal Article
In: Applied Soft Computing, vol. 169, pp. 112527, 2025, ISSN: 1568-4946.
@article{VERMA2025112527,
title = {MLP-GNAS: Meta-learning-based predictor-assisted Genetic Neural Architecture Search system},
author = {Sahil Verma and Prabhat Kumar and Jyoti Prakash Singh},
url = {https://www.sciencedirect.com/science/article/pii/S1568494624013012},
doi = {https://doi.org/10.1016/j.asoc.2024.112527},
issn = {1568-4946},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Applied Soft Computing},
volume = {169},
pages = {112527},
abstract = {Convolutional neural networks achieve state-of-the-art results on many image classification tasks. However, according to the No Free Lunch Theorem, no single model performs optimally for all the datasets. Hence, the manual development of these models using the hit-and-trial approach requires high computational overhead. Transfer learning mitigates this issue to some extent by transferring knowledge learned from similar domain tasks to target tasks through model architecture and weights. Yet, the model remains designed for the source task, and using the same model on the target task may result in over-parameterization or sub-optimal results. Henceforth, the current work proposes a meta-learning-based predictor-assisted genetic Neural Architecture Search (MLP-GNAS) system to automate the model generation process for plant disease detection tasks to validate its efficacy in real-world scenarios. To this cause, the MLP-GNAS system employs a meta-learning component to recommend the top 3 suitable models for a target dataset, followed by a genetic algorithm-based NAS to fine-tune the recommended model. In addition, the proposed approach uses a CNN-based performance predictor designed to discard models unlikely to surpass the optimal performance recorded thus far. Further, extensive comparative experiments demonstrate that MLP-GNAS-generated models outperform 14 state-of-the-art models on two distinct plant disease datasets with an accuracy of 96.94% and 92.04%. Moreover, further comparison with the manually fine-tuned and NAS-generated models reveals that the proposed system outperforms both to obtain an accuracy of 98.35% and 99.95% on respective datasets.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Convolutional neural networks achieve state-of-the-art results on many image classification tasks. However, according to the No Free Lunch Theorem, no single model performs optimally for all the datasets. Hence, the manual development of these models using the hit-and-trial approach requires high computational overhead. Transfer learning mitigates this issue to some extent by transferring knowledge learned from similar domain tasks to target tasks through model architecture and weights. Yet, the model remains designed for the source task, and using the same model on the target task may result in over-parameterization or sub-optimal results. Henceforth, the current work proposes a meta-learning-based predictor-assisted genetic Neural Architecture Search (MLP-GNAS) system to automate the model generation process for plant disease detection tasks to validate its efficacy in real-world scenarios. To this cause, the MLP-GNAS system employs a meta-learning component to recommend the top 3 suitable models for a target dataset, followed by a genetic algorithm-based NAS to fine-tune the recommended model. In addition, the proposed approach uses a CNN-based performance predictor designed to discard models unlikely to surpass the optimal performance recorded thus far. Further, extensive comparative experiments demonstrate that MLP-GNAS-generated models outperform 14 state-of-the-art models on two distinct plant disease datasets with an accuracy of 96.94% and 92.04%. Moreover, further comparison with the manually fine-tuned and NAS-generated models reveals that the proposed system outperforms both to obtain an accuracy of 98.35% and 99.95% on respective datasets.
3829.
Fu, Wei; Lou, Wenqi; Qin, Yunji; Gong, Lei; Wang, Chao; Zhou, Xuehai
MFNAS: Multi-fidelity Exploration in Neural Architecture Search with Stable Zero-Shot Proxy Proceedings Article
In: Hadfi, Rafik; Anthony, Patricia; Sharma, Alok; Ito, Takayuki; Bai, Quan (Ed.): PRICAI 2024: Trends in Artificial Intelligence, pp. 348–360, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-0116-5.
@inproceedings{10.1007/978-981-96-0116-5_29,
title = {MFNAS: Multi-fidelity Exploration in Neural Architecture Search with Stable Zero-Shot Proxy},
author = {Wei Fu and Wenqi Lou and Yunji Qin and Lei Gong and Chao Wang and Xuehai Zhou},
editor = {Rafik Hadfi and Patricia Anthony and Alok Sharma and Takayuki Ito and Quan Bai},
isbn = {978-981-96-0116-5},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {PRICAI 2024: Trends in Artificial Intelligence},
pages = {348–360},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Neural architecture search (NAS) automates the design of neural networks for specific tasks. Recently, zero-shot NAS has attracted much attention. Unlike traditional NAS, which relies on training to rank architectures, zero-shot NAS uses gradients or activation information to evaluate architecture performance. However, existing zero-shot NAS methods are limited by their inconsistent architecture ranking and the evaluation bias of their search algorithm, making it challenging to discover networks with high accuracy efficiently. To address this dilemma, this paper proposes an efficient and stable search framework for zero-shot NAS. Firstly, we design a stable zero-shot proxy, which achieves good consistency with network accuracy by utilizing filtered gradient information. On this basis, we employ a multi-fidelity evolutionary algorithm for efficient exploration. This algorithm utilizes multi-fidelity proxies to correct the bias towards certain types of networks and enhances the ability to distinguish high-performing architectures, achieving rapid convergence through performance-directed multi-point crossover and mutation. Experimental results conducted on NATS-Bench demonstrate that our framework can discover high-performance architectures within minutes of GPU time, outperforming existing training-free and training-based NAS methods. The code is available at https://github.com/mine7777/MFNAS.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Neural architecture search (NAS) automates the design of neural networks for specific tasks. Recently, zero-shot NAS has attracted much attention. Unlike traditional NAS, which relies on training to rank architectures, zero-shot NAS uses gradients or activation information to evaluate architecture performance. However, existing zero-shot NAS methods are limited by their inconsistent architecture ranking and the evaluation bias of their search algorithm, making it challenging to discover networks with high accuracy efficiently. To address this dilemma, this paper proposes an efficient and stable search framework for zero-shot NAS. Firstly, we design a stable zero-shot proxy, which achieves good consistency with network accuracy by utilizing filtered gradient information. On this basis, we employ a multi-fidelity evolutionary algorithm for efficient exploration. This algorithm utilizes multi-fidelity proxies to correct the bias towards certain types of networks and enhances the ability to distinguish high-performing architectures, achieving rapid convergence through performance-directed multi-point crossover and mutation. Experimental results conducted on NATS-Bench demonstrate that our framework can discover high-performance architectures within minutes of GPU time, outperforming existing training-free and training-based NAS methods. The code is available at https://github.com/mine7777/MFNAS.
3828.
Wang, Yu; Fan, Jiahao; Sun, Yanan
Classification of sewer pipe defects based on an automatically designed convolutional neural network Journal Article
In: Expert Systems with Applications, vol. 264, pp. 125806, 2025, ISSN: 0957-4174.
@article{WANG2025125806,
title = {Classification of sewer pipe defects based on an automatically designed convolutional neural network},
author = {Yu Wang and Jiahao Fan and Yanan Sun},
url = {https://www.sciencedirect.com/science/article/pii/S0957417424026733},
doi = {https://doi.org/10.1016/j.eswa.2024.125806},
issn = {0957-4174},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Expert Systems with Applications},
volume = {264},
pages = {125806},
abstract = {Accurate classification of sewer pipe defects allows for timely prevention of system failures, preventing environmental pollution caused by sewage leakage, thereby safeguarding public health and ensuring urban infrastructure operates normally. However, existing methods with deep convolutional neural networks not only require a significant amount of time to design network architectures, but also demand substantial computational resources. Therefore, this paper automatically designed high-performance and lightweight AutoSewerNet based on neural architecture search (NAS) for classification of sewer pipe defects. First, Super-net is designed to enrich the diversity of search space and include high-performance network architecture. Second, to reduce the search time of NAS, a gradient-based search strategy is designed. Third, AutoSewerNet is designed to be lightweight and perform real-time inspections of sewer pipes. Fourth, weight balance is introduced to solve the imbalanced dataset problem. Experimental results demonstrate that AutoSewerNet achieved an F1-score of 0.6251 on the benchmark dataset, which is far superior to that of ResNet-50 (F1-score: 0.4523) and InceptionV3 (F1-score: 0.4611). Moreover, AutoSewerNet requires only 11.6% of VGG-16. Thus, AutoSewerNet is better than state-of-the-art methods. Our source code and models are anonymously available at https://anonymous.4open.science/r/ASN-26AB/.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Accurate classification of sewer pipe defects allows for timely prevention of system failures, preventing environmental pollution caused by sewage leakage, thereby safeguarding public health and ensuring urban infrastructure operates normally. However, existing methods with deep convolutional neural networks not only require a significant amount of time to design network architectures, but also demand substantial computational resources. Therefore, this paper automatically designed high-performance and lightweight AutoSewerNet based on neural architecture search (NAS) for classification of sewer pipe defects. First, Super-net is designed to enrich the diversity of search space and include high-performance network architecture. Second, to reduce the search time of NAS, a gradient-based search strategy is designed. Third, AutoSewerNet is designed to be lightweight and perform real-time inspections of sewer pipes. Fourth, weight balance is introduced to solve the imbalanced dataset problem. Experimental results demonstrate that AutoSewerNet achieved an F1-score of 0.6251 on the benchmark dataset, which is far superior to that of ResNet-50 (F1-score: 0.4523) and InceptionV3 (F1-score: 0.4611). Moreover, AutoSewerNet requires only 11.6% of VGG-16. Thus, AutoSewerNet is better than state-of-the-art methods. Our source code and models are anonymously available at https://anonymous.4open.science/r/ASN-26AB/.
3827.
Zhong, Rui; Xu, Yuefeng; Zhang, Chao; Yu, Jun
Efficient multiplayer battle game optimizer for numerical optimization and adversarial robust neural architecture search Journal Article
In: Alexandria Engineering Journal, vol. 113, pp. 150-168, 2025, ISSN: 1110-0168.
@article{ZHONG2025150,
title = {Efficient multiplayer battle game optimizer for numerical optimization and adversarial robust neural architecture search},
author = {Rui Zhong and Yuefeng Xu and Chao Zhang and Jun Yu},
url = {https://www.sciencedirect.com/science/article/pii/S1110016824014935},
doi = {https://doi.org/10.1016/j.aej.2024.11.035},
issn = {1110-0168},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Alexandria Engineering Journal},
volume = {113},
pages = {150-168},
abstract = {This paper introduces a novel metaheuristic algorithm, known as the efficient multiplayer battle game optimizer (EMBGO), specifically designed for addressing complex numerical optimization tasks. The motivation behind this research stems from the need to rectify identified shortcomings in the original MBGO, particularly in search operators during the movement phase, as revealed through ablation experiments. EMBGO mitigates these limitations by integrating the movement and battle phases to simplify the original optimization framework and improve search efficiency. Besides, two efficient search operators: differential mutation and Lévy flight are introduced to increase the diversity of the population. To evaluate the performance of EMBGO comprehensively and fairly, numerical experiments are conducted on benchmark functions such as CEC2017, CEC2020, and CEC2022, as well as engineering problems. Twelve well-established MA approaches serve as competitor algorithms for comparison. Furthermore, we apply the proposed EMBGO to the complex adversarial robust neural architecture search (ARNAS) tasks and explore its robustness and scalability. The experimental results and statistical analyses confirm the efficiency and effectiveness of EMBGO across various optimization tasks. As a potential optimization technique, EMBGO holds promise for diverse applications in real-world problems and deep learning scenarios. The source code of EMBGO is made available in https://github.com/RuiZhong961230/EMBGO.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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This paper introduces a novel metaheuristic algorithm, known as the efficient multiplayer battle game optimizer (EMBGO), specifically designed for addressing complex numerical optimization tasks. The motivation behind this research stems from the need to rectify identified shortcomings in the original MBGO, particularly in search operators during the movement phase, as revealed through ablation experiments. EMBGO mitigates these limitations by integrating the movement and battle phases to simplify the original optimization framework and improve search efficiency. Besides, two efficient search operators: differential mutation and Lévy flight are introduced to increase the diversity of the population. To evaluate the performance of EMBGO comprehensively and fairly, numerical experiments are conducted on benchmark functions such as CEC2017, CEC2020, and CEC2022, as well as engineering problems. Twelve well-established MA approaches serve as competitor algorithms for comparison. Furthermore, we apply the proposed EMBGO to the complex adversarial robust neural architecture search (ARNAS) tasks and explore its robustness and scalability. The experimental results and statistical analyses confirm the efficiency and effectiveness of EMBGO across various optimization tasks. As a potential optimization technique, EMBGO holds promise for diverse applications in real-world problems and deep learning scenarios. The source code of EMBGO is made available in https://github.com/RuiZhong961230/EMBGO.
3826.
Man, Wenxing; Xu, Liming; He, Chunlin
Evolutionary architecture search for generative adversarial networks using an aging mechanism-based strategy Journal Article
In: Neural Networks, vol. 181, pp. 106877, 2025, ISSN: 0893-6080.
@article{MAN2025106877,
title = {Evolutionary architecture search for generative adversarial networks using an aging mechanism-based strategy},
author = {Wenxing Man and Liming Xu and Chunlin He},
url = {https://www.sciencedirect.com/science/article/pii/S0893608024008050},
doi = {https://doi.org/10.1016/j.neunet.2024.106877},
issn = {0893-6080},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Neural Networks},
volume = {181},
pages = {106877},
abstract = {Generative Adversarial Networks (GANs) have emerged as a key technology in artificial intelligence, especially in image generation. However, traditionally hand-designed GAN architectures often face significant training stability challenges, which are effectively addressed by our Evolutionary Neural Architecture Search (ENAS) algorithm for GANs, named EAMGAN. This one-shot model automates the design of GAN architectures and employs an Operation Importance Metric (OIM) to enhance training stability. It also incorporates an aging mechanism to optimize the selection process during architecture search. Additionally, the use of a non-dominated sorting algorithm ensures the generation of Pareto-optimal solutions, promoting diversity and preventing premature convergence. We evaluated our method on benchmark datasets, and the results demonstrate that EAMGAN is highly competitive in terms of efficiency and performance. Our method identified an architecture achieving Inception Scores (IS) of 8.83±0.13 and Fréchet Inception Distance (FID) of 9.55 on CIFAR-10 with only 0.66 GPU days. Results on the STL-10, CIFAR-100, and ImageNet32 datasets further demonstrate the robust portability of our architecture.},
keywords = {},
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}
Generative Adversarial Networks (GANs) have emerged as a key technology in artificial intelligence, especially in image generation. However, traditionally hand-designed GAN architectures often face significant training stability challenges, which are effectively addressed by our Evolutionary Neural Architecture Search (ENAS) algorithm for GANs, named EAMGAN. This one-shot model automates the design of GAN architectures and employs an Operation Importance Metric (OIM) to enhance training stability. It also incorporates an aging mechanism to optimize the selection process during architecture search. Additionally, the use of a non-dominated sorting algorithm ensures the generation of Pareto-optimal solutions, promoting diversity and preventing premature convergence. We evaluated our method on benchmark datasets, and the results demonstrate that EAMGAN is highly competitive in terms of efficiency and performance. Our method identified an architecture achieving Inception Scores (IS) of 8.83±0.13 and Fréchet Inception Distance (FID) of 9.55 on CIFAR-10 with only 0.66 GPU days. Results on the STL-10, CIFAR-100, and ImageNet32 datasets further demonstrate the robust portability of our architecture.
3825.
Liu, Wenbo; Deng, Tao; An, Rui; Yan, Fei
DARTS-CGW: Research on Differentiable Neural Architecture Search Algorithm Based on Coarse Gradient Weighting Proceedings Article
In: Lin, Zhouchen; Cheng, Ming-Ming; He, Ran; Ubul, Kurban; Silamu, Wushouer; Zha, Hongbin; Zhou, Jie; Liu, Cheng-Lin (Ed.): Pattern Recognition and Computer Vision, pp. 31–44, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-97-8502-5.
@inproceedings{10.1007/978-981-97-8502-5_3,
title = {DARTS-CGW: Research on Differentiable Neural Architecture Search Algorithm Based on Coarse Gradient Weighting},
author = {Wenbo Liu and Tao Deng and Rui An and Fei Yan},
editor = {Zhouchen Lin and Ming-Ming Cheng and Ran He and Kurban Ubul and Wushouer Silamu and Hongbin Zha and Jie Zhou and Cheng-Lin Liu},
url = {https://link.springer.com/chapter/10.1007/978-981-97-8502-5_3},
isbn = {978-981-97-8502-5},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Pattern Recognition and Computer Vision},
pages = {31–44},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Differential architecture search (DARTS) has emerged as a prominent research area, yet it grapples with a longstanding challenge: the discretization discrepancy problem. This issue directly impedes the search for an optimal model architecture and undermines search algorithm performance. To alleviate this issue, we propose a novel coarse gradient weighting algorithm. Our proposed algorithm has the capability to simulate the discretization process, wherein the architectural parameters move toward both ends. And we integrate this discretization process into the training phase of the architectural parameters, enabling the model to adapt to the discretization process in a trial-and-error fashion. Specifically, based on the architectural parameters in training, we divide the candidate operations into two regions, i.e., the easy-to-select region and the hard-to-be-selected region. The different weighting strategies are implemented in different regions so that the architectural parameters are pushed to the ends. The processed architecture parameters are used for training, which is equivalent to introducing the discretization process into the search phase. Additionally, we use the coarse gradient algorithm to optimize the updating process of the weighting algorithm and theoretically justify the rationality of the coarse gradient weighting algorithm. Extensive experimental results demonstrate that our proposed method can improve the performance of the searched model and make DARTS more robust without adding additional search time.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Differential architecture search (DARTS) has emerged as a prominent research area, yet it grapples with a longstanding challenge: the discretization discrepancy problem. This issue directly impedes the search for an optimal model architecture and undermines search algorithm performance. To alleviate this issue, we propose a novel coarse gradient weighting algorithm. Our proposed algorithm has the capability to simulate the discretization process, wherein the architectural parameters move toward both ends. And we integrate this discretization process into the training phase of the architectural parameters, enabling the model to adapt to the discretization process in a trial-and-error fashion. Specifically, based on the architectural parameters in training, we divide the candidate operations into two regions, i.e., the easy-to-select region and the hard-to-be-selected region. The different weighting strategies are implemented in different regions so that the architectural parameters are pushed to the ends. The processed architecture parameters are used for training, which is equivalent to introducing the discretization process into the search phase. Additionally, we use the coarse gradient algorithm to optimize the updating process of the weighting algorithm and theoretically justify the rationality of the coarse gradient weighting algorithm. Extensive experimental results demonstrate that our proposed method can improve the performance of the searched model and make DARTS more robust without adding additional search time.
3824.
Garcia-Garcia, Cosijopii; Derbel, Bilel; Morales-Reyes, Alicia; Escalante, Hugo Jair
Speeding up the Multi-objective NAS Through Incremental Learning Proceedings Article
In: Martínez-Villaseñor, Lourdes; Ochoa-Ruiz, Gilberto (Ed.): Advances in Soft Computing, pp. 3–15, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-75543-9.
@inproceedings{10.1007/978-3-031-75543-9_1,
title = {Speeding up the Multi-objective NAS Through Incremental Learning},
author = {Cosijopii Garcia-Garcia and Bilel Derbel and Alicia Morales-Reyes and Hugo Jair Escalante},
editor = {Lourdes Martínez-Villaseñor and Gilberto Ochoa-Ruiz},
url = {https://link.springer.com/chapter/10.1007/978-3-031-75543-9_1},
isbn = {978-3-031-75543-9},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Advances in Soft Computing},
pages = {3–15},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Deep neural networks (DNNs), particularly convolutional neural networks (CNNs), have garnered significant attention in recent years for addressing a wide range of challenges in image processing and computer vision. Neural architecture search (NAS) has emerged as a crucial field aiming to automate the design and configuration of CNN models. In this paper, we propose a novel strategy to speed up the performance estimation of neural architectures by gradually increasing the size of the training set used for evaluation as the search progresses. We evaluate this approach using the CGP-NASV2 model, a multi-objective NAS method, on the CIFAR-100 dataset. Experimental results demonstrate a notable acceleration in the search process, achieving a speedup of 4.6 times compared to the baseline. Despite using limited data in the early stages, our proposed method effectively guides the search towards competitive architectures. This study highlights the efficacy of leveraging lower-fidelity estimates in NAS and paves the way for further research into accelerating the design of efficient CNN architectures.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep neural networks (DNNs), particularly convolutional neural networks (CNNs), have garnered significant attention in recent years for addressing a wide range of challenges in image processing and computer vision. Neural architecture search (NAS) has emerged as a crucial field aiming to automate the design and configuration of CNN models. In this paper, we propose a novel strategy to speed up the performance estimation of neural architectures by gradually increasing the size of the training set used for evaluation as the search progresses. We evaluate this approach using the CGP-NASV2 model, a multi-objective NAS method, on the CIFAR-100 dataset. Experimental results demonstrate a notable acceleration in the search process, achieving a speedup of 4.6 times compared to the baseline. Despite using limited data in the early stages, our proposed method effectively guides the search towards competitive architectures. This study highlights the efficacy of leveraging lower-fidelity estimates in NAS and paves the way for further research into accelerating the design of efficient CNN architectures.
3823.
Solis-Martin, David; Galan-Paez, Juan; Borrego-Diaz, Joaquin
Bayesian Model Selection Pruning in Predictive Maintenance Proceedings Article
In: Quintián, Héctor; Corchado, Emilio; Lora, Alicia Troncoso; García, Hilde Pérez; Pérez, Esteban Jove; Rolle, José Luis Calvo; de Pisón, Francisco Javier Martínez; Bringas, Pablo García; Álvarez, Francisco Martínez; Herrero, Álvaro; Fosci, Paolo (Ed.): Hybrid Artificial Intelligent Systems, pp. 263–274, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-74183-8.
@inproceedings{10.1007/978-3-031-74183-8_22b,
title = {Bayesian Model Selection Pruning in Predictive Maintenance},
author = {David Solis-Martin and Juan Galan-Paez and Joaquin Borrego-Diaz},
editor = {Héctor Quintián and Emilio Corchado and Alicia Troncoso Lora and Hilde Pérez García and Esteban Jove Pérez and José Luis Calvo Rolle and Francisco Javier Martínez de Pisón and Pablo García Bringas and Francisco Martínez Álvarez and Álvaro Herrero and Paolo Fosci},
url = {https://link.springer.com/chapter/10.1007/978-3-031-74183-8_22},
isbn = {978-3-031-74183-8},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Hybrid Artificial Intelligent Systems},
pages = {263–274},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Deep Neural Network architecture design significantly impacts the final model performance. The process of searching for optimal architectures, known as Neural Architecture Search (NAS), involves training and evaluating an important number of models. Therefore, mechanisms to reduce the resources required for NAS are highly valuable.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep Neural Network architecture design significantly impacts the final model performance. The process of searching for optimal architectures, known as Neural Architecture Search (NAS), involves training and evaluating an important number of models. Therefore, mechanisms to reduce the resources required for NAS are highly valuable.
3822.
Solis-Martin, David; Galan-Paez, Juan; Borrego-Diaz, Joaquin
Bayesian Model Selection Pruning in Predictive Maintenance Proceedings Article
In: Quintián, Héctor; Corchado, Emilio; Lora, Alicia Troncoso; García, Hilde Pérez; Pérez, Esteban Jove; Rolle, José Luis Calvo; de Pisón, Francisco Javier Martínez; Bringas, Pablo García; Álvarez, Francisco Martínez; Herrero, Álvaro; Fosci, Paolo (Ed.): Hybrid Artificial Intelligent Systems, pp. 263–274, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-74183-8.
@inproceedings{10.1007/978-3-031-74183-8_22,
title = {Bayesian Model Selection Pruning in Predictive Maintenance},
author = {David Solis-Martin and Juan Galan-Paez and Joaquin Borrego-Diaz},
editor = {Héctor Quintián and Emilio Corchado and Alicia Troncoso Lora and Hilde Pérez García and Esteban Jove Pérez and José Luis Calvo Rolle and Francisco Javier Martínez de Pisón and Pablo García Bringas and Francisco Martínez Álvarez and Álvaro Herrero and Paolo Fosci},
url = {https://link.springer.com/chapter/10.1007/978-3-031-74183-8_22},
isbn = {978-3-031-74183-8},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Hybrid Artificial Intelligent Systems},
pages = {263–274},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Deep Neural Network architecture design significantly impacts the final model performance. The process of searching for optimal architectures, known as Neural Architecture Search (NAS), involves training and evaluating an important number of models. Therefore, mechanisms to reduce the resources required for NAS are highly valuable.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep Neural Network architecture design significantly impacts the final model performance. The process of searching for optimal architectures, known as Neural Architecture Search (NAS), involves training and evaluating an important number of models. Therefore, mechanisms to reduce the resources required for NAS are highly valuable.
3821.
Wang, Weibo; Li, Hua
NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search Journal Article
In: Biomedical Signal Processing and Control, vol. 100, pp. 107022, 2025, ISSN: 1746-8094.
@article{WANG2025107022,
title = {NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search},
author = {Weibo Wang and Hua Li},
url = {https://www.sciencedirect.com/science/article/pii/S1746809424010802},
doi = {https://doi.org/10.1016/j.bspc.2024.107022},
issn = {1746-8094},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {100},
pages = {107022},
abstract = {In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.
3820.
Wu, Zhenpeng; Chen, Jiamin; Al-Sabri, Raeed; Oloulade, Babatounde Moctard; Gao, Jianliang
Asymmetric augmented paradigm-based graph neural architecture search Journal Article
In: Information Processing & Management, vol. 62, no. 1, pp. 103897, 2025, ISSN: 0306-4573.
@article{WU2025103897,
title = {Asymmetric augmented paradigm-based graph neural architecture search},
author = {Zhenpeng Wu and Jiamin Chen and Raeed Al-Sabri and Babatounde Moctard Oloulade and Jianliang Gao},
url = {https://www.sciencedirect.com/science/article/pii/S0306457324002565},
doi = {https://doi.org/10.1016/j.ipm.2024.103897},
issn = {0306-4573},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Information Processing & Management},
volume = {62},
number = {1},
pages = {103897},
abstract = {In most scenarios of graph-based tasks, graph neural networks (GNNs) are trained end-to-end with labeled samples. Labeling graph samples, a time-consuming and expert-dependent process, leads to huge costs. Graph data augmentations can provide a promising method to expand labeled samples cheaply. However, graph data augmentations will damage the capacity of GNNs to distinguish non-isomorphic graphs during the supervised graph representation learning process. How to utilize graph data augmentations to expand labeled samples while preserving the capacity of GNNs to distinguish non-isomorphic graphs is a challenging research problem. To address the above problem, we abstract a novel asymmetric augmented paradigm in this paper and theoretically prove that it offers a principled approach. The asymmetric augmented paradigm can preserve the capacity of GNNs to distinguish non-isomorphic graphs while utilizing augmented labeled samples to improve the generalization capacity of GNNs. To be specific, the asymmetric augmented paradigm will utilize similar yet distinct asymmetric weights to classify the real sample and augmented sample, respectively. To systemically explore the benefits of asymmetric augmented paradigm under different GNN architectures, rather than studying individual asymmetric augmented GNN (A2GNN) instance, we then develop an auto-search engine called Asymmetric Augmented Graph Neural Architecture Search (A2GNAS) to save human efforts. We empirically validate our asymmetric augmented paradigm on multiple graph classification benchmarks, and demonstrate that representative A2GNN instances automatically discovered by our A2GNAS method achieve state-of-the-art performance compared with competitive baselines. Our codes are available at: https://github.com/csubigdata-Organization/A2GNAS.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In most scenarios of graph-based tasks, graph neural networks (GNNs) are trained end-to-end with labeled samples. Labeling graph samples, a time-consuming and expert-dependent process, leads to huge costs. Graph data augmentations can provide a promising method to expand labeled samples cheaply. However, graph data augmentations will damage the capacity of GNNs to distinguish non-isomorphic graphs during the supervised graph representation learning process. How to utilize graph data augmentations to expand labeled samples while preserving the capacity of GNNs to distinguish non-isomorphic graphs is a challenging research problem. To address the above problem, we abstract a novel asymmetric augmented paradigm in this paper and theoretically prove that it offers a principled approach. The asymmetric augmented paradigm can preserve the capacity of GNNs to distinguish non-isomorphic graphs while utilizing augmented labeled samples to improve the generalization capacity of GNNs. To be specific, the asymmetric augmented paradigm will utilize similar yet distinct asymmetric weights to classify the real sample and augmented sample, respectively. To systemically explore the benefits of asymmetric augmented paradigm under different GNN architectures, rather than studying individual asymmetric augmented GNN (A2GNN) instance, we then develop an auto-search engine called Asymmetric Augmented Graph Neural Architecture Search (A2GNAS) to save human efforts. We empirically validate our asymmetric augmented paradigm on multiple graph classification benchmarks, and demonstrate that representative A2GNN instances automatically discovered by our A2GNAS method achieve state-of-the-art performance compared with competitive baselines. Our codes are available at: https://github.com/csubigdata-Organization/A2GNAS.
3819.
Jiang, Zhiying; Liu, Risheng; Yang, Shuzhou; Zhang, Zengxi; Fan, Xin
DRNet: Learning a dynamic recursion network for chaotic rain streak removal Journal Article
In: Pattern Recognition, vol. 158, pp. 111004, 2025, ISSN: 0031-3203.
@article{JIANG2025111004,
title = {DRNet: Learning a dynamic recursion network for chaotic rain streak removal},
author = {Zhiying Jiang and Risheng Liu and Shuzhou Yang and Zengxi Zhang and Xin Fan},
url = {https://www.sciencedirect.com/science/article/pii/S0031320324007556},
doi = {https://doi.org/10.1016/j.patcog.2024.111004},
issn = {0031-3203},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Pattern Recognition},
volume = {158},
pages = {111004},
abstract = {Image deraining refers to removing the visible rain streaks to restore the rain-free scenes. Existing methods rely on manually crafted networks to model the distribution of rain streaks. However, complex scenes disrupt the uniformity of rain streak characteristics assumed in ideal conditions, resulting in rain streaks of varying directions, intensities, and brightness intersecting within the same scene, challenging the deep learning based deraining performance. To address the chaotic rain streak removal, we handle the rain streaks with similar distribution characteristics in the same layer and employ a dynamic recursive mechanism to extract and unveil them progressively. Specifically, we employ neural architecture search to determine the models of different rain streaks. To avoid the loss of texture details associated with overly deep structures, we integrate multi-scale modeling and cross-scale recruitment within the dynamic structure. Considering the application of real-world scenes, we incorporate contrastive training to improve the generalization. Experimental results indicate superior performance in rain streak depiction compared to existing methods. Practical evaluation confirms its effectiveness in object detection and semantic segmentation tasks. Code is available at https://github.com/Jzy2017/DRNet.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Image deraining refers to removing the visible rain streaks to restore the rain-free scenes. Existing methods rely on manually crafted networks to model the distribution of rain streaks. However, complex scenes disrupt the uniformity of rain streak characteristics assumed in ideal conditions, resulting in rain streaks of varying directions, intensities, and brightness intersecting within the same scene, challenging the deep learning based deraining performance. To address the chaotic rain streak removal, we handle the rain streaks with similar distribution characteristics in the same layer and employ a dynamic recursive mechanism to extract and unveil them progressively. Specifically, we employ neural architecture search to determine the models of different rain streaks. To avoid the loss of texture details associated with overly deep structures, we integrate multi-scale modeling and cross-scale recruitment within the dynamic structure. Considering the application of real-world scenes, we incorporate contrastive training to improve the generalization. Experimental results indicate superior performance in rain streak depiction compared to existing methods. Practical evaluation confirms its effectiveness in object detection and semantic segmentation tasks. Code is available at https://github.com/Jzy2017/DRNet.
3818.
Rahman, Abdur; Street, Jason; Wooten, James; Marufuzzaman, Mohammad; Gude, Veera G.; Buchanan, Randy; Wang, Haifeng
MoistNet: Machine vision-based deep learning models for wood chip moisture content measurement Journal Article
In: Expert Systems with Applications, vol. 259, pp. 125363, 2025, ISSN: 0957-4174.
@article{Rahman_2025,
title = {MoistNet: Machine vision-based deep learning models for wood chip moisture content measurement},
author = {Abdur Rahman and Jason Street and James Wooten and Mohammad Marufuzzaman and Veera G. Gude and Randy Buchanan and Haifeng Wang},
url = {http://dx.doi.org/10.1016/j.eswa.2024.125363},
doi = {10.1016/j.eswa.2024.125363},
issn = {0957-4174},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Expert Systems with Applications},
volume = {259},
pages = {125363},
publisher = {Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3817.
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.
@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}
}
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.
2024
3816.
Souza, Melwin D.; Prabhu, G. Ananth; Kumara, Varuna
Advanced Breast Cancer Detection Using Spatial Attention and Neural Architecture Search (SANAS-Net) Journal Article
In: SN Computer Science , vol. 6, 2024.
@article{nokey,
title = {Advanced Breast Cancer Detection Using Spatial Attention and Neural Architecture Search (SANAS-Net)},
author = {
Melwin D. Souza and G. Ananth Prabhu and Varuna Kumara
},
url = {https://link.springer.com/article/10.1007/s42979-024-03568-9},
year = {2024},
date = {2024-12-20},
urldate = {2024-12-20},
journal = {SN Computer Science },
volume = {6},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3815.
Wang, Ruochen
Automated Machine Learning in the Era of Large Foundation Models PhD Thesis
2024.
@phdthesis{nokey,
title = {Automated Machine Learning in the Era of Large Foundation Models},
author = {Wang, Ruochen},
url = {https://escholarship.org/content/qt1vc4421f/qt1vc4421f.pdf},
year = {2024},
date = {2024-12-16},
urldate = {2024-12-16},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
3814.
Alaoui, Ali Omari; Boutahir, Mohamed Khalifa; Bahi, Omaima El; Hessane, Abdelaaziz; Farhaoui, Yousef; Allaoui, Ahmad El
Accelerating deep learning model development—towards scalable automated architecture generation for optimal model design Journal Article
In: Multimedia Tools and Applications , 2024.
@article{Alaoui-mta24a,
title = {Accelerating deep learning model development—towards scalable automated architecture generation for optimal model design},
author = {Ali Omari Alaoui and Mohamed Khalifa Boutahir and Omaima El Bahi and Abdelaaziz Hessane and Yousef Farhaoui and Ahmad El Allaoui },
url = {https://link.springer.com/article/10.1007/s11042-024-20481-8},
year = {2024},
date = {2024-12-16},
urldate = {2024-12-16},
journal = {Multimedia Tools and Applications },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3813.
Tan, Zhiwen; Guo, Daqi; Chen, Junan; Chen, Lei
M2M-Net: multi-objective neural architecture search using dynamic M2M population decomposition Journal Article
In: Neural Computing and Applications, 2024.
@article{tan-nca24a,
title = {M2M-Net: multi-objective neural architecture search using dynamic M2M population decomposition},
author = {
Zhiwen Tan and Daqi Guo and Junan Chen and Lei Chen
},
url = {https://link.springer.com/article/10.1007/s00521-024-10595-3},
year = {2024},
date = {2024-12-02},
urldate = {2024-12-02},
journal = { Neural Computing and Applications},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3812.
Cheng, Jian; Jiang, Jinbo; Kang, Haidong; Ma, Lianbo
A Hybrid Neural Architecture Search Algorithm Optimized via Lifespan-PSO for Coal Mine Image Recognition Journal Article
In: Preprints, 2024.
@article{202412.2176,
title = {A Hybrid Neural Architecture Search Algorithm Optimized via Lifespan-PSO for Coal Mine Image Recognition},
author = {Jian Cheng and Jinbo Jiang and Haidong Kang and Lianbo Ma},
url = {https://doi.org/10.20944/preprints202412.2176.v1},
doi = {10.20944/preprints202412.2176.v1},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
journal = {Preprints},
publisher = {Preprints},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3811.
Fouchal, Amel; Tikhamarine, Yazid; Benbouras, Mohammed Amin; Souag-Gamane, Doudja; Heddam, Salim
In: Modeling Earth Systems and Environment , vol. 11, 2024.
@article{Fouchal-mese24a,
title = {Biological oxygen demand prediction using artificial neural network and random forest models enhanced by the neural architecture search algorithm},
author = {Amel Fouchal and Yazid Tikhamarine and Mohammed Amin Benbouras and Doudja Souag-Gamane and Salim Heddam },
url = {https://link.springer.com/article/10.1007/s40808-024-02178-x},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
journal = {Modeling Earth Systems and Environment },
volume = {11},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3810.
Sombo, Bem; Apeh, Simon Tooswem; Edeoghon, Isi Arthur
An Artificial Neural Network-Based Caller Authentication and Identification Algorithm in Cellular Communication Networks Journal Article
In: International Journal of Applied Methods in Electronics and Computers, 2024.
@article{nokey,
title = {An Artificial Neural Network-Based Caller Authentication and Identification Algorithm in Cellular Communication Networks},
author = { Bem Sombo and Simon Tooswem Apeh and Isi Arthur Edeoghon },
url = {https://www.ijamec.org/index.php/ijamec/article/view/432},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
journal = { International Journal of Applied Methods in Electronics and Computers},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3809.
Shi, Xinjie; Guo, Chenxia; Yang, Ruifeng; Song, Yizhe
Adaptive evolutionary neural architecture search based on one-dimensional convolutional neural network for electric rudder fault diagnosis Journal Article
In: Measurement Science and Technology, vol. 36, no. 1, pp. 016038, 2024.
@article{Shi_2025,
title = {Adaptive evolutionary neural architecture search based on one-dimensional convolutional neural network for electric rudder fault diagnosis},
author = {Xinjie Shi and Chenxia Guo and Ruifeng Yang and Yizhe Song},
url = {https://dx.doi.org/10.1088/1361-6501/ad962e},
doi = {10.1088/1361-6501/ad962e},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
journal = {Measurement Science and Technology},
volume = {36},
number = {1},
pages = {016038},
publisher = {IOP Publishing},
abstract = {The electric rudder is the core actuator of the flight control system. Fault diagnosis of rudders is essential for the production and repair of rudders. While existing methods for rudder fault diagnosis are effective, the manual design of neural network models is a time-consuming and challenging process. Therefore, this paper proposes a fault diagnosis framework for the electric rudder based on an adaptive evolutionary neural architecture search (AENAS-FD). AENAS-FD employs an adaptive strategy to guide the evolution of a one-dimensional convolutional neural network towards achieving optimal diagnostic accuracy. This adaptive strategy adjusts the relevant parameters of the genetic operator based on the relationship between individual and population fitness. This leads to improved algorithm search performance and mitigates premature convergence. The experiments on the real electric rudder dataset demonstrate that AENAS-FD can generate superior network architectures for diagnosing rudder faults, exhibiting better diagnostic accuracy when compared to manually designed networks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The electric rudder is the core actuator of the flight control system. Fault diagnosis of rudders is essential for the production and repair of rudders. While existing methods for rudder fault diagnosis are effective, the manual design of neural network models is a time-consuming and challenging process. Therefore, this paper proposes a fault diagnosis framework for the electric rudder based on an adaptive evolutionary neural architecture search (AENAS-FD). AENAS-FD employs an adaptive strategy to guide the evolution of a one-dimensional convolutional neural network towards achieving optimal diagnostic accuracy. This adaptive strategy adjusts the relevant parameters of the genetic operator based on the relationship between individual and population fitness. This leads to improved algorithm search performance and mitigates premature convergence. The experiments on the real electric rudder dataset demonstrate that AENAS-FD can generate superior network architectures for diagnosing rudder faults, exhibiting better diagnostic accuracy when compared to manually designed networks.
3808.
Zhou, Ao; Yang, Jianlei; Qi, Yingjie; Qiao, Tong; Shi, Yumeng; Duan, Cenlin; Zhao, Weisheng; Hu, Chunming
HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices Journal Article
In: IEEE Transactions on Computers, vol. 73, no. 12, pp. 2693-2707, 2024, ISSN: 1557-9956.
@article{10644077,
title = { HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices },
author = {Ao Zhou and Jianlei Yang and Yingjie Qi and Tong Qiao and Yumeng Shi and Cenlin Duan and Weisheng Zhao and Chunming Hu},
url = {https://doi.ieeecomputersociety.org/10.1109/TC.2024.3449108},
doi = {10.1109/TC.2024.3449108},
issn = {1557-9956},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
journal = {IEEE Transactions on Computers},
volume = {73},
number = {12},
pages = {2693-2707},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {Graph Neural Networks (GNNs) are becoming increasingly popular for graph-based learning tasks such as point cloud processing due to their state-of-the-art (SOTA) performance. Nevertheless, the research community has primarily focused on improving model expressiveness, lacking consideration of how to design efficient GNN models for edge scenarios with real-time requirements and limited resources. Examining existing GNN models reveals varied execution across platforms and frequent Out-Of-Memory (OOM) problems, highlighting the need for hardware-aware GNN design. To address this challenge, this work proposes a novel hardware-aware graph neural architecture search framework tailored for resource constraint edge devices, namely HGNAS. To achieve hardware awareness, HGNAS integrates an efficient GNN hardware performance predictor that evaluates the latency and peak memory usage of GNNs in milliseconds. Meanwhile, we study GNN memory usage during inference and offer a peak memory estimation method, enhancing the robustness of architecture evaluations when combined with predictor outcomes. Furthermore, HGNAS constructs a fine-grained design space to enable the exploration of extreme performance architectures by decoupling the GNN paradigm. In addition, the multi-stage hierarchical search strategy is leveraged to facilitate the navigation of huge candidates, which can reduce the single search time to a few GPU hours. To the best of our knowledge, HGNAS is the first automated GNN design framework for edge devices, and also the first work to achieve hardware awareness of GNNs across different platforms. Extensive experiments across various applications and edge devices have proven the superiority of HGNAS. It can achieve up to a $10.6boldsymboltimes$10.6× speedup and an $82.5%$82.5% peak memory reduction with negligible accuracy loss compared to DGCNN on ModelNet40.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Graph Neural Networks (GNNs) are becoming increasingly popular for graph-based learning tasks such as point cloud processing due to their state-of-the-art (SOTA) performance. Nevertheless, the research community has primarily focused on improving model expressiveness, lacking consideration of how to design efficient GNN models for edge scenarios with real-time requirements and limited resources. Examining existing GNN models reveals varied execution across platforms and frequent Out-Of-Memory (OOM) problems, highlighting the need for hardware-aware GNN design. To address this challenge, this work proposes a novel hardware-aware graph neural architecture search framework tailored for resource constraint edge devices, namely HGNAS. To achieve hardware awareness, HGNAS integrates an efficient GNN hardware performance predictor that evaluates the latency and peak memory usage of GNNs in milliseconds. Meanwhile, we study GNN memory usage during inference and offer a peak memory estimation method, enhancing the robustness of architecture evaluations when combined with predictor outcomes. Furthermore, HGNAS constructs a fine-grained design space to enable the exploration of extreme performance architectures by decoupling the GNN paradigm. In addition, the multi-stage hierarchical search strategy is leveraged to facilitate the navigation of huge candidates, which can reduce the single search time to a few GPU hours. To the best of our knowledge, HGNAS is the first automated GNN design framework for edge devices, and also the first work to achieve hardware awareness of GNNs across different platforms. Extensive experiments across various applications and edge devices have proven the superiority of HGNAS. It can achieve up to a $10.6boldsymboltimes$10.6× speedup and an $82.5%$82.5% peak memory reduction with negligible accuracy loss compared to DGCNN on ModelNet40.
3807.
Li, Gai; Cao, Chunhong; Fu, Huawei; Li, Xingxing; Gao, Xieping
Modeling Functional Brain Networks for ADHD via Spatial Preservation-Based Neural Architecture Search Journal Article
In: IEEE J Biomed Health Inform , 2024.
@article{Li-BHI24a,
title = { Modeling Functional Brain Networks for ADHD via Spatial Preservation-Based Neural Architecture Search },
author = {Gai Li and Chunhong Cao and Huawei Fu and Xingxing Li and Xieping Gao
},
url = {https://pubmed.ncbi.nlm.nih.gov/39167518/},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
journal = { IEEE J Biomed Health Inform },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3806.
Min, Yongzhi; Jing, Qinglong; Li, Yaxing
Method for rail surface defect detection based on neural network architecture search Journal Article
In: Measurement Science and Technology, 2024.
@article{min-mst24a,
title = {Method for rail surface defect detection based on neural network architecture search},
author = { Yongzhi Min and Qinglong Jing and Yaxing Li },
url = {https://iopscience.iop.org/article/10.1088/1361-6501/ad9048},
doi = {10.1088/1361-6501/ad9048},
year = {2024},
date = {2024-11-20},
urldate = {2024-11-20},
journal = { Measurement Science and Technology},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3805.
Sun, Hongmin; Kan, Ao; Liu, Jianhao; Du, Wei
HG-search: multi-stage search for heterogeneous graph neural networks Journal Article
In: Applied Intelligence , 2024.
@article{sun-applieint-24a,
title = {HG-search: multi-stage search for heterogeneous graph neural networks},
author = {
Hongmin Sun and Ao Kan and Jianhao Liu and Wei Du
},
url = {https://link.springer.com/article/10.1007/s10489-024-06058-w},
year = {2024},
date = {2024-11-19},
urldate = {2024-11-19},
journal = {Applied Intelligence },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3804.
Kundu, Akash; Sarkar, Aritra; Sadhu, Abhishek
KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search Journal Article
In: EPJ Quantum Technology , 2024.
@article{Kundu-EPJQT24a,
title = {KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search},
author = {
Akash Kundu and Aritra Sarkar and Abhishek Sadhu
},
url = {https://epjquantumtechnology.springeropen.com/articles/10.1140/epjqt/s40507-024-00289-z},
year = {2024},
date = {2024-11-12},
urldate = {2024-11-12},
journal = {EPJ Quantum Technology },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3803.
Jia Ma,; Ma, Xinru; Li, Chulian; Li, Tongyan
Vehicle-drone collaborative distribution path planning based on neural architecture search under the influence of carbon emissions Journal Article
In: Discover Computing , vol. 27, 2024.
@article{ma-dc24a,
title = {Vehicle-drone collaborative distribution path planning based on neural architecture search under the influence of carbon emissions},
author = {
Jia Ma, and Xinru Ma and Chulian Li and Tongyan Li
},
url = {https://link.springer.com/article/10.1007/s10791-024-09469-y},
year = {2024},
date = {2024-11-11},
urldate = {2024-11-11},
journal = {Discover Computing },
volume = {27},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3802.
Patil, Tejwardhan
Quantum-Enhanced Neural Architecture Search (Q-NAS) Technical Report
2024.
@techreport{nokey,
title = {Quantum-Enhanced Neural Architecture Search (Q-NAS)},
author = {Tejwardhan Patil},
url = {https://openreview.net/pdf/eacda89e6b55648ad4512decd7c711e3be063033.pdf},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
3801.
Li, Jialin; Cao, Xuan; Chen, Renxiang; Zhao, Chengying; Huang, Xianzhen
Network architecture search methods for constructing efficient fault diagnosis models in rotating machinery Journal Article
In: Measurement Science and Technology, vol. 36, no. 1, pp. 016144, 2024.
@article{Li_2025,
title = {Network architecture search methods for constructing efficient fault diagnosis models in rotating machinery},
author = {Jialin Li and Xuan Cao and Renxiang Chen and Chengying Zhao and Xianzhen Huang},
url = {https://dx.doi.org/10.1088/1361-6501/ad8f4c},
doi = {10.1088/1361-6501/ad8f4c},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
journal = {Measurement Science and Technology},
volume = {36},
number = {1},
pages = {016144},
publisher = {IOP Publishing},
abstract = {The development of high-performance fault diagnosis models for specific tasks requires substantial expertise. Neural architecture search (NAS) offers a promising solution, but most NAS methodologies are hampered by lengthy search durations and low efficiency, and few researchers have applied these methods within the fault diagnosis domain. This paper introduces a novel differentiable architecture search method tailored for constructing efficient fault diagnosis models for rotating machinery, designed to rapidly and effectively search for network models suitable for specific datasets. Specifically, this study constructs a completely new and advanced search space, incorporating various efficient, lightweight convolutional operations to reduce computational complexity. To enhance the stability of the differentiable network architecture search process and reduce fluctuations in model accuracy, this study proposes a novel Multi-scale Pyramid Squeeze Attention module. This module aids in the learning of richer multi-scale feature representations and adaptively recalibrates the weights of multi-dimensional channel attention. The proposed method was validated on two rotating machinery fault datasets, demonstrating superior performance compared to manually designed networks and general network search methods, with notably improved diagnostic effectiveness.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The development of high-performance fault diagnosis models for specific tasks requires substantial expertise. Neural architecture search (NAS) offers a promising solution, but most NAS methodologies are hampered by lengthy search durations and low efficiency, and few researchers have applied these methods within the fault diagnosis domain. This paper introduces a novel differentiable architecture search method tailored for constructing efficient fault diagnosis models for rotating machinery, designed to rapidly and effectively search for network models suitable for specific datasets. Specifically, this study constructs a completely new and advanced search space, incorporating various efficient, lightweight convolutional operations to reduce computational complexity. To enhance the stability of the differentiable network architecture search process and reduce fluctuations in model accuracy, this study proposes a novel Multi-scale Pyramid Squeeze Attention module. This module aids in the learning of richer multi-scale feature representations and adaptively recalibrates the weights of multi-dimensional channel attention. The proposed method was validated on two rotating machinery fault datasets, demonstrating superior performance compared to manually designed networks and general network search methods, with notably improved diagnostic effectiveness.
3800.
Gao, Jianhua; Liu, Zeming; Wang, Yizhuo; Ji, Weixing
RaNAS: Resource-Aware Neural Architecture Search for Edge Computing Journal Article
In: ACM Trans. Archit. Code Optim., 2024, ISSN: 1544-3566, (Just Accepted).
@article{10.1145/3703353,
title = {RaNAS: Resource-Aware Neural Architecture Search for Edge Computing},
author = {Jianhua Gao and Zeming Liu and Yizhuo Wang and Weixing Ji},
url = {https://doi.org/10.1145/3703353},
doi = {10.1145/3703353},
issn = {1544-3566},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
journal = {ACM Trans. Archit. Code Optim.},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Neural architecture search (NAS) for edge devices is often time-consuming because of long-latency deploying and testing on edge devices. The ability to accurately predict the computation cost and memory requirement for convolutional neural networks (CNNs) in advance holds substantial value. Existing work primarily relies on analytical models, which can result in high prediction errors. This paper proposes a resource-aware NAS (RaNAS) model based on various features. Additionally, a new graph neural network is introduced to predict inference latency and maximum memory requirements for CNNs on edge devices. Experimental results show that, within the error bound of ±1%, RaNAS achieves an accuracy improvement of approximately 8% for inference latency prediction and about 25% for maximum memory occupancy prediction over the state-of-the-art approaches.},
note = {Just Accepted},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neural architecture search (NAS) for edge devices is often time-consuming because of long-latency deploying and testing on edge devices. The ability to accurately predict the computation cost and memory requirement for convolutional neural networks (CNNs) in advance holds substantial value. Existing work primarily relies on analytical models, which can result in high prediction errors. This paper proposes a resource-aware NAS (RaNAS) model based on various features. Additionally, a new graph neural network is introduced to predict inference latency and maximum memory requirements for CNNs on edge devices. Experimental results show that, within the error bound of ±1%, RaNAS achieves an accuracy improvement of approximately 8% for inference latency prediction and about 25% for maximum memory occupancy prediction over the state-of-the-art approaches.