AutoML | Literature on Neural Architecture Search

@article{10042973,

title = {DGL: Device Generic Latency Model for Neural Architecture Search on Mobile Devices},

author = {Q. Wang and S. Zhang},

url = {https://www.computer.org/csdl/journal/tm/5555/01/10042973/1KJs8PnAasw},

doi = {10.1109/TMC.2023.3244170},

issn = {1558-0660},

year  = {2023},

date = {2023-02-01},

urldate = {5555-02-01},

journal = {IEEE Transactions on Mobile Computing},

number = {01},

pages = {1-14},

publisher = {IEEE Computer Society},

address = {Los Alamitos, CA, USA},

abstract = {The low-cost Neural Architecture Search (NAS) for lightweight networks working on massive mobile devices is essential for fast-developing ICT technology. Current NAS work can not search on unseen devices without latency sampling, which is a big obstacle to the implementation of NAS on mobile devices. In this paper, we overcome this challenge by proposing the Device Generic Latency (DGL) model. By absorbing processor modeling technology, the proposed DGL formula maps the parameters in the interval theory to the seven static configuration parameters of the device. And to make the formula more practical, we refine it to low-cost form by decreasing the number of configuration parameters to four. Then based on this formula, the DGL model is proposed which introduces the network parameters predictor and accuracy predictor to work with the DGL formula to predict the network latency. We propose the DGL-based NAS framework to enable fast searches without latency sampling. Extensive experiments results validate that the DGL model can achieve more accurate latency predictions than existing NAS latency predictors on unseen mobile devices. When configured with current state-of-the-art predictors, DGL-based NAS can search for architectures with higher accuracy that meet the latency limit than other NAS implementations, while using less training time and prediction time. Our work shed light on how to adopt domain knowledge into NAS topic and play important role in low-cost NAS on mobile devices.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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2951.

Wei, Lanning; Zhao, Huan; He, Zhiqiang; Yao, Quanming

Neural Architecture Search for GNN-Based Graph Classification Journal Article

In: ACM Trans. Inf. Syst., 2023, ISSN: 1046-8188, (Just Accepted).

Abstract | Links | BibTeX

@article{10.1145/3584945,

title = {Neural Architecture Search for GNN-Based Graph Classification},

author = {Lanning Wei and Huan Zhao and Zhiqiang He and Quanming Yao},

url = {https://doi.org/10.1145/3584945},

doi = {10.1145/3584945},

issn = {1046-8188},

year  = {2023},

date = {2023-02-01},

urldate = {2023-02-01},

journal = {ACM Trans. Inf. Syst.},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Graph classification is an important problem with applications across many domains, for which the graph neural networks (GNNs) have been state-of-the-art (SOTA) methods. In the literature, to adopt GNNs for the graph classification task, there are two groups of methods: global pooling and hierarchical pooling. The global pooling methods obtain the graph representation vectors by globally pooling all the node embeddings together at the end of several GNN layers, while the hierarchical pooling methods provide one extra pooling operation between the GNN layers to extract the hierarchical information and improve the graph representations. Both global and hierarchical pooling methods are effective in different scenarios. Due to highly diverse applications, it is challenging to design data-specific pooling methods with human expertise. To address this problem, we propose PAS (Pooling Architecture Search) to design adaptive pooling architectures by using the neural architecture search (NAS). To enable the search space design, we propose a unified pooling framework consisting of four modules: Aggregation, Pooling, Readout, and Merge. Two variants PAS-G and PAS-NE are provided to design the pooling operations in different scales. A set of candidate operations are designed in the search space on top of this framework, and then existing human-designed pooling methods, including global and hierarchical ones, can be incorporated. To enable efficient search, a coarsening strategy is developed to continuously relax the search space, and then a differentiable search method can be adopted. We conduct extensive experiments on six real-world datasets, including the large-scale datasets MR and ogbg-molhiv. Experimental results in this paper demonstrate the effectiveness and efficiency of the proposed PAS in designing the pooling architectures for graph classification. Besides, the Top-1 performance on two Open Graph Benchmark (OGB) datasets further indicates the utility of PAS when facing diverse realistic data. The implementation of PAS is available at: https://github.com/AutoML-Research/PAS.},

note = {Just Accepted},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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Graph classification is an important problem with applications across many domains, for which the graph neural networks (GNNs) have been state-of-the-art (SOTA) methods. In the literature, to adopt GNNs for the graph classification task, there are two groups of methods: global pooling and hierarchical pooling. The global pooling methods obtain the graph representation vectors by globally pooling all the node embeddings together at the end of several GNN layers, while the hierarchical pooling methods provide one extra pooling operation between the GNN layers to extract the hierarchical information and improve the graph representations. Both global and hierarchical pooling methods are effective in different scenarios. Due to highly diverse applications, it is challenging to design data-specific pooling methods with human expertise. To address this problem, we propose PAS (Pooling Architecture Search) to design adaptive pooling architectures by using the neural architecture search (NAS). To enable the search space design, we propose a unified pooling framework consisting of four modules: Aggregation, Pooling, Readout, and Merge. Two variants PAS-G and PAS-NE are provided to design the pooling operations in different scales. A set of candidate operations are designed in the search space on top of this framework, and then existing human-designed pooling methods, including global and hierarchical ones, can be incorporated. To enable efficient search, a coarsening strategy is developed to continuously relax the search space, and then a differentiable search method can be adopted. We conduct extensive experiments on six real-world datasets, including the large-scale datasets MR and ogbg-molhiv. Experimental results in this paper demonstrate the effectiveness and efficiency of the proposed PAS in designing the pooling architectures for graph classification. Besides, the Top-1 performance on two Open Graph Benchmark (OGB) datasets further indicates the utility of PAS when facing diverse realistic data. The implementation of PAS is available at: https://github.com/AutoML-Research/PAS.

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2950.

Ci, Yuanzheng

Efficient Methods for the Design and Training of Neural Networks PhD Thesis

2023.

Links | BibTeX

2949.

(Ed.)

Neural Architecture Search for Wide Spectrum Adversarial Robustness Collection

2023.

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2948.

Fang, Yuchu; Li, Wenzhong; Zeng, Yao; Zheng, Yang; Hu, Zheng; Lu, Sanglu

PatchNAS: Repairing DNNs in Deployment with Patched Network Architecture Search Conference

https://ojs.aaai.org/index.php/AAAI/article/download/26730/26502, 2023.

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2947.

Jia, Liang; Tian, Ye; Zhang, Junguo

Neural architecture search based on packed samples for identifying animals in camera trap images Journal Article

In: Neural Computing and Applications (2023), 2023.

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2946.

Tchetchenian, Ari; Zhu, Yanming; Zhang, Fan; O’Donnell, Lauren J.; Song, Yang; Meijering, Erik

A comparison of manual and automated neural architecture search for white matter tract segmentation Journal Article

In: Scientific Reports , 2023.

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2945.

Yue, Zhixiong; Zhang, Yu; Liang, Jie

Learning Conflict-Noticed Architecture for Multi-Task Learning Miscellaneous

2023.

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2944.

Chang, Chen-Chia; Pan, Jingyu; Xie, Zhiyao; Li, Yaguang; Lin, Yishuang; Hu, Jiang; Chen, Yiran

Fully Automated Machine Learning Model Development for Analog Placement Quality Prediction Proceedings Article

In: 2023 Asia and South Pacific Design Automation Conference (ASP-DAC), 2023.

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2943.

Zhang, Jian; Gong, Xuan; Liu, YuXiao; Wang, Wei; Wang, Lei; Zhang, BaoChang

Bandit neural architecture search based on performance evaluation for operation selection Journal Article

In: Science China Technological Sciences, 2023.

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2942.

Asadi, Mehdi; Poursalim, Fatemeh; Loni, Mohammad; Daneshtalab, Masoud; Sjödin, Mikael; Gharehbaghi, Arash

Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search Technical Manual

2023.

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2941.

Zhang, Jian; Gong, Xuan; Liu, YuXiao; Wang, Wei; Wang, Lei; Zhang, BaoChang

Bandit neural architecture search based on performance evaluation for operation selection Journal Article

In: Science China Technological Sciences 2023, 2023.

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2940.

Franchini, Giorgia; Ruggiero, Valeria; Porta, Federica; Zanni, Luca

Neural architecture search via standard machine learning methodologies Journal Article

In: Mathematics in Engineering, 2023.

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2939.

Wang, Ziyan; Qi, Feng; Zou, Liming

Continuous Evolution for Efficient Neural Architecture Search Based on Improved NSGA-III Algorithm Proceedings Article

In: Sun, Jiande; Wang, Yue; Huo, Mengyao; Xu, Lexi (Ed.): Signal and Information Processing, Networking and Computers, pp. 979–986, Springer Nature Singapore, Singapore, 2023, ISBN: 978-981-19-3387-5.

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2938.

Rajarajeswari, S.; Patil, Annapurna P.; Madhyastha, Aditya; Jaitly, Akshat; Jha, Himangshu Shekhar; Bhave, Sahil Rajesh; Das, Mayukh; Pradeep, N. S.

Design and Develop Hardware Aware DNN for Faster Inference Proceedings Article

In: Ärai, Kohei" (Ed.): Intelligent Systems and Applications, pp. 309–318, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-16075-2.

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2937.

Liang, Jingkang; Liao, Yixiao; Li, Weihua

Differentiable Architecture Searched Network with Tree-Structured Parzen Estimators for Rotating Machinery Fault Diagnosis Proceedings Article

In: Zhang, Hao; Feng, Guojin; Wang, Hongjun; Gu, Fengshou; Sinha, Jyoti K. (Ed.): Proceedings of IncoME-VI and TEPEN 2021, pp. 959–970, Springer International Publishing, Cham, 2023, ISBN: 978-3-030-99075-6.

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2936.

Liu, Yang; Liang, Xinle; Luo, Jiahuan; He, Yuanqin; Chen, Tianjian; Yao, Quanming; Yang, Qiang

Cross-Silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems Book Chapter

In: Razavi-Far, Roozbeh; Wang, Boyu; Taylor, Matthew E.; Yang, Qiang (Ed.): Federated and Transfer Learning, pp. 57–86, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-11748-0.

Abstract | Links | BibTeX

@inbook{Liu2023,

title = {Cross-Silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems},

author = {Yang Liu and Xinle Liang and Jiahuan Luo and Yuanqin He and Tianjian Chen and Quanming Yao and Qiang Yang},

editor = {Roozbeh Razavi-Far and Boyu Wang and Matthew E. Taylor and Qiang Yang},

url = {https://doi.org/10.1007/978-3-031-11748-0_4},

doi = {10.1007/978-3-031-11748-0_4},

isbn = {978-3-031-11748-0},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Federated and Transfer Learning},

pages = {57--86},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {In many cooperative systems (i.e. autonomous vehicles, robotics, hospital networks), data are privately and heterogeneously distributed among devices with various computational constraints, and no party has a global view of data or device distribution. Federated Neural Architecture Search (FedNAS) was previously proposed to adapt Neural Architecture Search (NAS) into Federated Learning (FL) to provide both privacy and model performance to such uninspectable and heterogeneous systems. However, these approaches mostly apply to scenarios where parties share the same data attributes and comparable computation resources. In this chapter, we present Self-supervised Vertical Federated Neural Architecture Search (SS-VFNAS) for automating FL where participants have heterogeneous data and resource constraints, a common cross-silo scenario. SS-VFNAS not only simultaneously optimizes all parties' model architecture and parameters for the best global performance under a vertical FL (VFL) framework using only a small set of aligned and labeled data, but also preserves each party's local optimal model architecture under a self-supervised NAS framework. We demonstrate that SS-VFNAS is a promising framework of superior performance, communication efficiency and privacy, and is capable of generating high-performance and highly-transferable heterogeneous architectures with only limited overlapping samples, providing practical solutions for designing collaborative systems with both limited data and resource constraints.},

keywords = {},

pubstate = {published},

tppubtype = {inbook}

}

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2935.

Deng, TianJin; Wu, Jia

Efficient graph neural architecture search using Monte Carlo Tree search and prediction network Journal Article

In: Expert Systems with Applications, vol. 213, pp. 118916, 2023, ISSN: 0957-4174.

Abstract | Links | BibTeX

@article{DENG2023118916,

title = {Efficient graph neural architecture search using Monte Carlo Tree search and prediction network},

author = {TianJin Deng and Jia Wu},

url = {https://www.sciencedirect.com/science/article/pii/S0957417422019340},

doi = {https://doi.org/10.1016/j.eswa.2022.118916},

issn = {0957-4174},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Expert Systems with Applications},

volume = {213},

pages = {118916},

abstract = {Graph Neural Networks (GNNs) have emerged recently as a powerful way of dealing with non-Euclidean data on graphs, such as social networks and citation networks. Despite their success, obtaining optimal graph neural networks requires immense manual work and domain knowledge. Inspired by the strong searching capability of neural architecture search in CNN, a few attempts automatically search optimal GNNs that rival the best human-invented architectures. However, existing Graph Neural Architecture Search (GNAS) approaches face two challenges: (1) Sampling GNNs across the entire search space results in low search efficiency, particularly in large search spaces. (2) It is pretty costly to evaluate GNNs by training architectures from scratch. To overcome these challenges, this paper proposes an Efficient Graph Neural Architecture Search (EGNAS) method based on Monte Carlo Tree Search (MCTS) and a prediction network. Specifically, EGNAS first uses MCTS to recursively partition the entire search space into good or bad search regions. Then, the reinforcement learning-based search strategy (also called the agent) is applied to sample GNNs in those good search regions, which prevents overly exploring complex architectures and bad-performance regions, thus improving sampling efficiency. To reduce the evaluation cost, we use a prediction network to estimate the performance of GNNs. We alternately use ground-truth accuracy (by training GNNs from scratch) and prediction accuracy (by the prediction network) to update the search strategy to avoid inaccuracies caused by long-term use of the prediction network. Furthermore, to improve the training efficiency and stability, the agent is trained by a variant of Proximal Policy Optimization. Experiments show that EGNAS can search for better GNNs in the promising search region in a shorter search time, with an accuracy of 83.5%, 73.3%, 79.6%, and 94.5% on Cora, Citeseer, Pubmed, and Photo datasets, respectively In particular, compared to the most popular GNAS algorithm, our EGNAS-NP without using the prediction network achieves an accuracy of 83.6% on Cora, 73.5% on Citeseer, 79.9% on Pubmed, and 94.6% on Photo, with a relative improvement of 0.6%, 0.2%, 0.7%, and 0.6%.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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Graph Neural Networks (GNNs) have emerged recently as a powerful way of dealing with non-Euclidean data on graphs, such as social networks and citation networks. Despite their success, obtaining optimal graph neural networks requires immense manual work and domain knowledge. Inspired by the strong searching capability of neural architecture search in CNN, a few attempts automatically search optimal GNNs that rival the best human-invented architectures. However, existing Graph Neural Architecture Search (GNAS) approaches face two challenges: (1) Sampling GNNs across the entire search space results in low search efficiency, particularly in large search spaces. (2) It is pretty costly to evaluate GNNs by training architectures from scratch. To overcome these challenges, this paper proposes an Efficient Graph Neural Architecture Search (EGNAS) method based on Monte Carlo Tree Search (MCTS) and a prediction network. Specifically, EGNAS first uses MCTS to recursively partition the entire search space into good or bad search regions. Then, the reinforcement learning-based search strategy (also called the agent) is applied to sample GNNs in those good search regions, which prevents overly exploring complex architectures and bad-performance regions, thus improving sampling efficiency. To reduce the evaluation cost, we use a prediction network to estimate the performance of GNNs. We alternately use ground-truth accuracy (by training GNNs from scratch) and prediction accuracy (by the prediction network) to update the search strategy to avoid inaccuracies caused by long-term use of the prediction network. Furthermore, to improve the training efficiency and stability, the agent is trained by a variant of Proximal Policy Optimization. Experiments show that EGNAS can search for better GNNs in the promising search region in a shorter search time, with an accuracy of 83.5%, 73.3%, 79.6%, and 94.5% on Cora, Citeseer, Pubmed, and Photo datasets, respectively In particular, compared to the most popular GNAS algorithm, our EGNAS-NP without using the prediction network achieves an accuracy of 83.6% on Cora, 73.5% on Citeseer, 79.9% on Pubmed, and 94.6% on Photo, with a relative improvement of 0.6%, 0.2%, 0.7%, and 0.6%.

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2934.

Souquet, Léo; Shvai, Nadiya; Llanza, Arcadi; Nakib, Amir

Convolutional neural network architecture search based on fractal decomposition optimization algorithm Journal Article

In: Expert Systems with Applications, vol. 213, pp. 118947, 2023, ISSN: 0957-4174.

Abstract | Links | BibTeX

@article{SOUQUET2023118947,

title = {Convolutional neural network architecture search based on fractal decomposition optimization algorithm},

author = {Léo Souquet and Nadiya Shvai and Arcadi Llanza and Amir Nakib},

url = {https://www.sciencedirect.com/science/article/pii/S0957417422019650},

doi = {https://doi.org/10.1016/j.eswa.2022.118947},

issn = {0957-4174},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Expert Systems with Applications},

volume = {213},

pages = {118947},

abstract = {This paper presents a new approach to design the architecture and optimize the hyperparameters of a deep convolutional neural network (CNN) via of the Fractal Decomposition Algorithm (FDA). This optimization algorithm was recently proposed to solve continuous optimization problems. It is based on a geometric fractal decomposition that divides the search space while searching for the best solution possible. As FDA is effective in single-objective optimization, in this work we aim to prove that it can also be successfully applied to fine-tuning deep neural network architectures. Moreover, a new formulation based on bi-level optimization is proposed to separate the architecture search composed of discrete parameters from hyperparameters’ optimization. This is motivated by the fact that automating the construction of deep neural architecture has been an important focus over recent years as manual construction is considerably time-consuming, error-prone, and requires in-depth knowledge. To solve the bi-level problem thus formulated, a random search is performed aiming to create a set of candidate architectures. Then, the best ones are finetuned using FDA. CIFAR-10 and CIFAR-100 benchmarks were used to evaluate the performance of the proposed approach. The results obtained are among the state of the art in the corresponding class of networks (low number of parameters and chained-structured CNN architectures). The results are emphasized by the fact that the whole process was performed using low computing power with only 3 NVIDIA V100 GPUs. The source code is available at https://github.com/alc1218/Convolutional-Neural-Network-Architecture-Search-Based-on-Fractal-Decomposition-Optimization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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2933.

Wen, Yingpeng; Yu, Weijiang; Li, Dongsheng; Du, Jiangsu; Huang, Dan; Xiao, Nong

CosNAS: Enhancing estimation on cosmological parameters via neural architecture search Journal Article

In: New Astronomy, vol. 99, pp. 101955, 2023, ISSN: 1384-1076.

Abstract | Links | BibTeX

@article{WEN2023101955,

title = {CosNAS: Enhancing estimation on cosmological parameters via neural architecture search},

author = {Yingpeng Wen and Weijiang Yu and Dongsheng Li and Jiangsu Du and Dan Huang and Nong Xiao},

url = {https://www.sciencedirect.com/science/article/pii/S1384107622001373},

doi = {https://doi.org/10.1016/j.newast.2022.101955},

issn = {1384-1076},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {New Astronomy},

volume = {99},

pages = {101955},

abstract = {A great challenge of cosmology is estimating the cosmological parameters of the universe. With the development of deep learning, scientists adopt 3D deep neural networks to estimate cosmological parameters from the large-scale dark matter distribution of the universe, but these methods are time-consuming to design and train neural networks. While neural architecture search is an emerging approach to estimate cosmological parameters with its capability of automatically designing neural networks, the 3D operations on a 3D dataset prohibit the usage of traditional neural architecture search methods, due to its overwhelming time and memory consumption. To tackle these issues, we propose an efficient method, CosNAS, that can automatically design neural networks with 2D operations to estimate the cosmological parameters. In addition, processing 3D data with 2D operations will inevitably cause the loss of spatial information, thus we propose an efficient SABlock to retain more 3D spatial information. We also customize a space-focused search space to focus on important information in the dark matter distribution. The experimental results indicate that our estimation of the cosmological parameters Ω, σ and n, can be applied to large-scale 3D dark matter distribution and speedup the network search by 800x. The average relative errors of cosmological parameter estimations are (0.00163, 0.00065, 0.00080), significantly decreasing the average error of estimation by 85.5% compared to previous work.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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2932.

Li, Chuanyou; Zhang, Kun; Li, Yifan; Shang, Jiangwei; Zhang, Xinyue; Qian, Lei

ANNA: Accelerating Neural Network Accelerator through software-hardware co-design for vertical applications in edge systems Journal Article

In: Future Generation Computer Systems, vol. 140, pp. 91-103, 2023, ISSN: 0167-739X.

Abstract | Links | BibTeX

@article{LI202391,

title = {ANNA: Accelerating Neural Network Accelerator through software-hardware co-design for vertical applications in edge systems},

author = {Chuanyou Li and Kun Zhang and Yifan Li and Jiangwei Shang and Xinyue Zhang and Lei Qian},

url = {https://www.sciencedirect.com/science/article/pii/S0167739X22003168},

doi = {https://doi.org/10.1016/j.future.2022.10.001},

issn = {0167-739X},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Future Generation Computer Systems},

volume = {140},

pages = {91-103},

abstract = {In promising edge systems, AI algorithms and their hardware implementations are often joint optimized as integrated solutions to solve end-to-end design problems. Joint optimization depends on a delicate co-design of software and hardware. According to our knowledge, current co-design methodologies are still coarse-grained. In this paper, we proposed ANNA: Accelerating Neural Network Accelerator through a novel software-hardware co-design methodology. ANNA is a framework composed of three components: ANNA-NAS (Neural Architecture Search), ANNA-ARCH (hardware ARCHitecture) and ANNA-PERF (PERFormance optimizer & evaluator). ANNA-NAS adopts a cell-wise structure and is designed to be hardware aware. It aims at generating a neural network having high inference accuracy and low inference latency. To avoid tremendous time costs, ANNA-NAS synthetically uses differentiable architecture search and early stopping techniques. ANNA-ARCH starts to be designed as long as the architecture search space is defined. Based on the cell-wise structure, ANNA-ARCH specifies its main body which includes Convolution units, Activation Router and Buffer Pool. To well support different neural networks that could be generated by ANNA-NAS, the detailed part of ANNA-ARCH is configurable. ANNA-PERF harmonizes the co-design of ANNA-NAS and ANNA-ARCH. It takes a neural network and a hardware architecture as inputs. After optimizing the mapping strategy between the neural network and hardware accelerator, it feeds back a cycle-accurate latency to ANNA-NAS. Aiming at image classification, we carried out the experiments on ImageNet. Experimental results demonstrate that without loss of much inference accuracy, ANNA wins a significant low inference latency through a harmonious software and hardware co-design.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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2931.

Zharikov, Ilia; Krivorotov, Ivan; Maximov, Egor; Korviakov, Vladimir; Letunovskiy, Alexey

Ä Review of One-Shot Neural Architecture Search Methods Proceedings Article

In: Kryzhanovsky, Boris; Dunin-Barkowski, Witali; Redko, Vladimir; Tiumentsev, Yury (Ed.): Ädvances in Neural Computation, Machine Learning, and Cognitive Research VI", pp. 130–147, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-19032-2.

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2930.

Kolganov, Pavel A.; Tiumentsev, Yury V.

Än Attempt to Formalize the Formulation of the Network Architecture Search Problem for Convolutional Neural Networks Proceedings Article

In: Kryzhanovsky, Boris; Dunin-Barkowski, Witali; Redko, Vladimir; Tiumentsev, Yury (Ed.): Ädvances in Neural Computation, Machine Learning, and Cognitive Research VI", pp. 550–556, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-19032-2.

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2929.

Graham-Knight, John Brandon; Bond, Corey; Najjaran, Homayoun; Lucet, Yves; Lasserre, Patricia

Predicting and explaining performance and diversity of neural network architecture for semantic segmentation Journal Article

In: Expert Systems with Applications, vol. 214, pp. 119101, 2023, ISSN: 0957-4174.

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2928.

Sun, Yanan; Yen, Gary G.; Zhang, Mengjie

End-to-End Performance Predictors Book Chapter

In: Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances, pp. 237–255, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-16868-0.

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2927.

Sun, Yanan; Yen, Gary G.; Zhang, Mengjie

Hybrid GA and PSO for Architecture Design Book Chapter

In: Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances, pp. 171–180, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-16868-0.

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2926.

Sun, Yanan; Yen, Gary G.; Zhang, Mengjie

Architecture Design for Skip-Connection Based CNNs Book Chapter

In: Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances, pp. 147–170, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-16868-0.

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2925.

Sun, Yanan; Yen, Gary G.; Zhang, Mengjie

Differential Evolution for Architecture Design Book Chapter

In: Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances, pp. 193–202, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-16868-0.

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2924.

Jing, Kun; Chen, Luoyu; Xu, Jungang

An architecture entropy regularizer for differentiable neural architecture search Journal Article

In: Neural Networks, vol. 158, pp. 111-120, 2023, ISSN: 0893-6080.

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2923.

Yao, Fengqin; Wang, Shengke; Ding, Laihui; Zhong, Guoqiang; Bullock, Leon Bevan; Xu, Zhiwei; Dong, Junyu

Lightweight network learning with Zero-Shot Neural Architecture Search for UAV images Journal Article

In: Knowledge-Based Systems, vol. 260, pp. 110142, 2023, ISSN: 0950-7051.

Abstract | Links | BibTeX

@article{YAO2023110142,

title = {Lightweight network learning with Zero-Shot Neural Architecture Search for UAV images},

author = {Fengqin Yao and Shengke Wang and Laihui Ding and Guoqiang Zhong and Leon Bevan Bullock and Zhiwei Xu and Junyu Dong},

url = {https://www.sciencedirect.com/science/article/pii/S0950705122012382},

doi = {https://doi.org/10.1016/j.knosys.2022.110142},

issn = {0950-7051},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Knowledge-Based Systems},

volume = {260},

pages = {110142},

abstract = {Lightweight Network Architecture is essential for autonomous and intelligent monitoring of Unmanned Aerial Vehicles (UAVs), such as in object detection, image segmentation, and crowd counting applications. The state-of-the-art lightweight network learning based on Neural Architecture Search (NAS) usually costs enormous computation resources. Alternatively, low-performance embedded platforms and high-resolution drone images pose a challenge for lightweight network learning. To alleviate this problem, this paper proposes a new lightweight object detection model, called GhostShuffleNet (GSNet), for UAV images, which is built based on Zero-Shot Neural Architecture Search. This paper also introduces the new components which compose GSNet, namely GhostShuffle units (loosely based on ShuffleNetV2) and the backbone GSmodel-L. Firstly, a lightweight search space is constructed with the GhostShuffle (GS) units to reduce the parameters and floating-point operations (FLOPs). Secondly, the parameters, FLOPs, layers, and memory access cost (MAC) as constraints add to search strategy on a Zero-Shot Neural structure search algorithm, which then searches for an optimal network GSmodel-L. Finally, the optimal GSmodel-L is used as the backbone network and a Ghost-PAN feature fusion module and detection heads are added to complete the design of the lightweight object detection network (GSNet). Extensive experiments are conducted on the VisDrone2019 (14.92%mAP) dataset and the our UAV-OUC-DET (8.38%mAP) dataset demonstrating the efficiency and effectiveness of GSNet. The completed code is available at: https://github.com/yfq-yy/GSNet.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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2922.

Wang, Wenna; Zhang, Xiuwei; Cui, Hengfei; Yin, Hanlin; Zhang, Yannnig

FP-DARTS: Fast parallel differentiable neural architecture search for image classification Journal Article

In: Pattern Recognition, vol. 136, pp. 109193, 2023, ISSN: 0031-3203.

Abstract | Links | BibTeX

@article{WANG2023109193,

title = {FP-DARTS: Fast parallel differentiable neural architecture search for image classification},

author = {Wenna Wang and Xiuwei Zhang and Hengfei Cui and Hanlin Yin and Yannnig Zhang},

url = {https://www.sciencedirect.com/science/article/pii/S0031320322006720},

doi = {https://doi.org/10.1016/j.patcog.2022.109193},

issn = {0031-3203},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Pattern Recognition},

volume = {136},

pages = {109193},

abstract = {Neural Architecture Search (NAS) has made remarkable progress in automatic machine learning. However, it still suffers massive computing overheads limiting its wide applications. In this paper, we present an efficient search method, Fast Parallel Differential Neural Architecture Search (FP-DARTS). The proposed method is carefully designed from three levels to construct and train the super-network. Firstly, at the operation-level, to reduce the computational burden, different from the standard DARTS search space (8 operations), we decompose the operation set into two non-overlapping operator sub-sets (4 operations for each). Adopting these two reduced search spaces, two over-parameterized sub-networks are constructed. Secondly, at the channel-level, the partially-connected strategy is adopted, where each sub-network only adopts partial channels. Then these two sub-networks construct a two-parallel-path super-network by addition. Thirdly, at the training-level, the binary gate is introduced to control whether a path participates in the super-network training. It may suffer an unfair issue when using softmax to select the best input for intermediate nodes across two operator sub-sets. To tackle this problem, the sigmoid function is introduced, which measures the performance of operations without compression. Extensive experiments demonstrate the effectiveness of the proposed algorithm. Specifically, FP-DARTS achieves 2.50% test error with only 0.08 GPU-days on CIFAR10, and a state-of-the-art top-1 error rate of 23.7% on ImageNet using only 2.44 GPU-days for search.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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2921.

Jin, Cong; Huang, Jinjie; Wei, Tianshu; Chen, Yuanjian

Neural architecture search based on dual attention mechanism for image classification Journal Article

In: Mathematical Biosciences and Engineering, vol. 20, no. 2, pp. 2691-2715, 2023, ISSN: 1551-0018.

Links | BibTeX

2920.

Jin, Yaochu; Zhu, Hangyu; Xu, Jinjin; Chen, Yang

Evolutionary Multi-objective Federated Learning Book Chapter

In: Federated Learning: Fundamentals and Advances, pp. 139–164, Springer Nature Singapore, Singapore, 2023, ISBN: 978-981-19-7083-2.

Abstract | Links | BibTeX

@inbook{Jin2023,

title = {Evolutionary Multi-objective Federated Learning},

author = {Yaochu Jin and Hangyu Zhu and Jinjin Xu and Yang Chen},

url = {https://link.springer.com/chapter/10.1007/978-981-19-7083-2_3},

doi = {10.1007/978-981-19-7083-2_3},

isbn = {978-981-19-7083-2},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Federated Learning: Fundamentals and Advances},

pages = {139--164},

publisher = {Springer Nature Singapore},

address = {Singapore},

abstract = {Different from model quantization and partial model uploads presented in the previous chapter, evolutionary federated learning, more specifically, evolutionary federated neural architecture search, aims to optimize the architecture of neural network models, thereby reducing the communication costs caused by frequent model transmissions, generating lightweight neural models that are better suited for mobile and other edge devices, and also enhancing the final global model performance. To achieve this, scalable and efficient encoding methods for deep neural architectures must be designed and evolved using multi-objective evolutionary algorithms. This chapter presents two multi-objective evolutionary algorithms for federated neural architecture search. The first one employs a probabilistic representation of deep neural architectures that describes the connectivity between two neighboring layers and simultaneously maximizing the performance and minimizing the complexity of the neural architectures using a multi-objective evolutionary algorithm. However, this evolutionary framework is not practical for real-time optimization of the neural architectures in a federated environment. To tackle this challenge, a real-time federated evolutionary neural architecture search is then introduced. In addition to adopting a different neural search space, a double sampling strategy, including sampling subnetworks from a pretrained supernet and sampling clients for model update, is proposed so that the performance of the neural architectures becomes more stable, and each client needs to train one local model in one communication round, thereby preventing sudden performance drops during the optimization and avoiding training multiple submodels in one communication round. This way, evolutionary neural architecture search is made practical for real-time real-world applications.},

keywords = {},

pubstate = {published},

tppubtype = {inbook}

}

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