AutoML | Literature on Neural Architecture Search

2348.

Thakur, Aditya Shankar; Awari, Akshar Bajrang; Lyu, Zimeng; Desell, Travis

Efficient Neuroevolution Using Island Repopulation and Simplex Hyperparameter Optimization Proceedings Article

In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1837-1842, 2023.

2347.

Sari, Marlindia Ike; Ibnugraha, Prajna Deshanta; Sani, Muhammad Ikhsan; Rizal, Moch Fahru; Hanifa, Fanni Husnul; Kurniawan, Aris Pujud

Initial Estimation Passenger Number using YOLO-NAS Proceedings Article

In: 2023 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics (ICoABCD), pp. 181-185, 2023.

Links | BibTeX

2346.

Wang, Xingyu; Ji, Junzhong

Adaptive particle swarm architecture search based on multi-level convolutions for functional brain network classification Proceedings Article

In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 654-659, 2023.

Links | BibTeX

2345.

Chen, Shijie; Chen, Shengbo; Lei, Zhou

RGA-Reid: Neural Architecture Search Based on Relation-Aware Global Attention for Person Re-Identification Proceedings Article

In: 2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 399-402, 2023.

Links | BibTeX

2344.

Li, Jianbo; Hou, Guirong; Xiang, Hong; Lei, Zhiwen; Chen, Shaomiao

An efficient neural architecture search based deep learning algorithm for surface defects detection of T-beam Journal Article

In: International Journal of Embedded Systems, vol. 16, no. 2, pp. 153-160, 2023.

Abstract | Links | BibTeX

2343.

Zhang, Tian; Li, Nan; Zhou, Yuee; Cai, Wei; Ma, Lianbo

Information extraction of Chinese medical electronic records via evolutionary neural architecture search Proceedings Article

In: 2023 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 396-405, 2023.

Links | BibTeX

2342.

Tian, Wensheng; Zhang, Lei; Chen, Shuangxi; Wang, Hu; Luo, Xiao

Work-in-Progress: Model Dependability Constrained Differentiable Architecture Search for Safety Critical DNN Tasks Proceedings Article

In: 2023 IEEE Real-Time Systems Symposium (RTSS), pp. 447-450, 2023.

Links | BibTeX

2341.

Minimizing the weight uncertainty in Neural Networks Using Bayesian Optimization Proceedings Article

In: 2023 7th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech), pp. 1-6, 2023.

Links | BibTeX

2340.

Wang, Qinsi; Ke, Jinghan; Liang, Zhi; Zhang, Sihai

MathNAS: If Blocks Have a Role in Mathematical Architecture Design Proceedings Article

In: Oh, Alice; Naumann, Tristan; Globerson, Amir; Saenko, Kate; Hardt, Moritz; Levine, Sergey (Ed.): Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023, 2023.

Links | BibTeX

2339.

Anand, R N; Singh, Rimjhim Padam; Gupta, Deepa; Palaniappan, Kannappan

Ship Detection in Satellite Images using You Only Look Once-Neural Architecture Search Proceedings Article

In: 2023 9th International Conference on Signal Processing and Communication (ICSC), pp. 463-468, 2023.

Links | BibTeX

2338.

Sarti, Simone; Lomurno, Eugenio; Matteucci, Matteo

Neural Architecture Transfer 2: A Paradigm for Improving Efficiency in Multi-Objective Neural Architecture Search Technical Report

2023.

Links | BibTeX

2337.

Yan, Shen

New Perspectives in Neural Architecture Search: Architecture Embeddings, Efficient Performance Estimations, and Their Applications PhD Thesis

2022.

Links | BibTeX

2336.

Oloulade, Babatounde Moctard; Gao, Jianliang; Chen, Jiamin; Al-Sabri, Raeed; Lyu, Tengfei

Neural predictor-based automated graph classifier framework Journal Article

In: Machine Learning, 2022.

Links | BibTeX

2335.

Wei, Fang; Zhenhao, Zhu; Zhou, Hong; Tao, Zhang; Jun, Sun; Xiaojun, Wu

Efficient automatically evolving convolutional neural network for image denoising Journal Article

In: Memetic Computing , 2022.

Links | BibTeX

2334.

Men, Yanger; Li, Jingtao

Image Steganography of convolutional neural network based on neural architecture search Technical Manual

2022.

Links | BibTeX

2333.

Fan, Liang; Wang, Handing

Surrogate-assisted evolutionary neural architecture search with network embedding Journal Article

In: Complex & Intelligent Systems (2022), 2022.

Links | BibTeX

2332.

Hou, Libin; Wang, Linyuan; Peng, Qi; Yan, Bin

Stochastic Gradient Estimator for Differentiable NAS Proceedings Article

In: OPT2022: 14th Annual Workshop on Optimization for Machine Learning, 2022.

Links | BibTeX

2331.

Torabi, Ali; Sharifi, Arash; Teshnehlab, Mohammad

Using Cartesian Genetic Programming Approach with New Crossover Technique to Design Convolutional Neural Networks Journal Article

In: Neural Processing Letters, 2022.

Links | BibTeX

2330.

Wang, Chunnan; Zhang, Kaixin; Wang, Hongzhi; Chen, Bozhou

Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Journal Article

In: ACM Trans. Knowl. Discov. Data, 2022, ISSN: 1556-4681, (Just Accepted).

Abstract | Links | BibTeX

@article{10.1145/3571285,

title = {Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search},

author = {Chunnan Wang and Kaixin Zhang and Hongzhi Wang and Bozhou Chen},

url = {https://doi.org/10.1145/3571285},

doi = {10.1145/3571285},

issn = {1556-4681},

year  = {2022},

date = {2022-12-01},

urldate = {2022-12-01},

journal = {ACM Trans. Knowl. Discov. Data},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN models have their own advantages, i.e., each of them puts forward many effective operations and achieves good prediction results in the real applications. If users can effectively utilize and combine these excellent operations integrating the advantages of existing models, then they may obtain more effective STGCN models thus create greater value using existing work. However, they fail to do so due to the lack of domain knowledge, and there is lack of automated system to help users to achieve this goal. In this paper, we fill this gap and propose Auto-STGCN algorithm, which makes use of existing models to automatically explore high-performance STGCN model for specific scenarios. Specifically, we design Unified-STGCN framework, which summarizes the operations of existing architectures, and use parameters to control the usage and characteristic attributes of each operation, so as to realize the parameterized representation of the STGCN architecture and the reorganization and fusion of advantages. Then, we present Auto-STGCN, an optimization method based on reinforcement learning, to quickly search the parameter search space provided by Unified-STGCN, and generate optimal STGCN models automatically. Extensive experiments on real-world benchmark datasets show that our Auto-STGCN can find STGCN models superior to existing STGCN models used for search space construction, which demonstrates the effectiveness of our proposed method.},

note = {Just Accepted},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Close

2329.

Cui, S.; Wang, J.; Gui, X.; Wang, T.; Ma, F.

AUTOMED: Automated Medical Risk Predictive Modeling on Electronic Health Records Proceedings Article

In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 948-953, IEEE Computer Society, Los Alamitos, CA, USA, 2022.

Abstract | Links | BibTeX

@inproceedings{9995209,

title = {AUTOMED: Automated Medical Risk Predictive Modeling on Electronic Health Records},

author = {S. Cui and J. Wang and X. Gui and T. Wang and F. Ma},

url = {https://doi.ieeecomputersociety.org/10.1109/BIBM55620.2022.9995209},

doi = {10.1109/BIBM55620.2022.9995209},

year  = {2022},

date = {2022-12-01},

urldate = {2022-12-01},

booktitle = {2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},

pages = {948-953},

publisher = {IEEE Computer Society},

address = {Los Alamitos, CA, USA},

abstract = {Electronic health records (EHR) have been widely applied to various tasks in the medical domain such as risk predictive modeling, which aims to predict further health conditions by analyzing patients&#x2019; historical EHR. Existing work mainly focuses on modeling the sequential and temporal characteristics of EHR data with advanced deep learning techniques. However, the network architectures of these models are all manually designed based on experts&#x2019; prior knowledge, which largely impedes non-experts from exploring this task. To address this issue, in this paper, we propose a novel automated risk prediction model named AUTOMED to automatically search the optimal model architecture for modeling the complex EHR data and improving the performance of the risk prediction task. In particular, we follow the idea of neural architecture search to design a search space that contains three separate searchable modules. Two of them are used for analyzing sequential and temporal features of EHR data, respectively. The third is to automatically fuse both features together. Besides these three modules, AUTOMED contains an embedding module and a prediction module. All the three searchable modules are jointly optimized in the search stage to derive the optimal model architecture. In such a way, the model design can be automatically achieved with few human interventions. Experimental results on three real-world datasets show that AUTOMED outperforms state-of-the-art baselines in terms of PR-AUC, F1, and Cohen&#x2019;s Kappa. Moreover, the ablation study shows that AUTOMED can obtain reasonable model architectures and offer useful insights to the future risk prediction model design.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

2328.

Chen, J.; Wu, Z.; Gao, J.; Hu, X.

Glycan Immunogenicity Prediction with Efficient Automatic Graph Neural Network Proceedings Article

In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 309-314, IEEE Computer Society, Los Alamitos, CA, USA, 2022.

Abstract | Links | BibTeX

@inproceedings{9995685,

title = {Glycan Immunogenicity Prediction with Efficient Automatic Graph Neural Network},

author = {J. Chen and Z. Wu and J. Gao and X. Hu},

url = {https://doi.ieeecomputersociety.org/10.1109/BIBM55620.2022.9995685},

doi = {10.1109/BIBM55620.2022.9995685},

year  = {2022},

date = {2022-12-01},

urldate = {2022-12-01},

booktitle = {2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},

pages = {309-314},

publisher = {IEEE Computer Society},

address = {Los Alamitos, CA, USA},

abstract = {Glycans play an indispensable role in various bio-logical processes, such as cancer and autoimmune diseases. The function of glycan is closely determined by its structure. Due to the branch and nonlinear properties of glycans, previous research treats the glycans graph structure as a topological graph to represent glycans data effectively. Graph neural networks (GNNs) are an efficient graph mining method and have many applications in bioinformatics. Therefore, researchers have successfully used handcrafted GNNs to predict glycan immunogenicity. However, a GNN architecture contains many different components, and designing GNN architectures for specific graphs in the bioinformatics field is time-consuming and expert-dependent. To address this challenge, we propose an efficient automatic graph neural network method called EAGNN that can efficiently and automatically construct GNN architecture for glycan immunogenicity prediction. We design an effective graph attention pooling (GAP) search space. We use differential architecture search to efficiently create the optimal GNN architecture in the search space to build the GNN model for glycan immunogenicity prediction. We test EAGNN on the data set SugarBase based on the glycan immunogenicity prediction task. The experiment results show that EAGNN can work more superiorly than the baseline model and achieve the best performance.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

2327.

Liu, Qingqing; Xianpeng Wang,; Wang, Yao; Song, Xiangman

Evolutionary convolutional neural network for image classification based on multi-objective genetic programming with leader–follower mechanism Journal Article

In: Complex & Intelligent Systems 2022, 2022.

Links | BibTeX

2326.

Termritthikun, Chakkrit; Jamtsho, Yeshi; Muneesawang, Paisarn; Zhao, Jia; Lee, Ivan

Evolutionary neural architecture search based on efficient CNN models population for image classification Journal Article

In: Multimedia Tools and Applications 2022, 2022.

Links | BibTeX

2325.

Rao, Xuan; Zhao, Bo; Liu, Derong

DNAD: Differentiable Neural ArchitectureDistillation Proceedings Article

In: IEEE TRANSACTION ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022.

Links | BibTeX

2324.

Wang, Wei; Zhang, Yongde; Zhu, Liqiang

DRF-DRC: dynamic receptive field and dense residual connections for model compression Journal Article

In: Cognitive Neurodynamics, 2022.

Links | BibTeX

2323.

Chitnis, Shubham; Hosseini, Ramtin; Xie, Pengtao

Brain tumor classification based on neural architecture search Proceedings Article

In: 2022.

Links | BibTeX

2322.

Gao, Zejun; Cao, Fei; He, Chuan; Feng, Xiaowei; Xu, Jianfeng; Qin, Jianqiang

Network Optimization Algorithm for Radar Active Jamming Identification Based on Neural Architecture Search Proceedings Article

In: Progress In Electromagnetics Research, 2022.

Links | BibTeX

2321.

Raj, Aditya; Sathish, Rakshith; Tandra Sarka and, Ramanathan Sethuraman; Sheet, Debdoot

Designing Deep Neural High-Density Compression Engines for Radiology Images Journal Article

In: Circuits, Systems, and Signal Processing, 2022.

Links | BibTeX

2320.

Wan, Yuting; Zhong, Yanfei; Ma, Ailong; Wang, Junjue; LiangpeiZhang,

E2SCNet: Efficient Multiobjective Evolutionary Automatic Search for Remote Sensing Image Scene Classification Network Architecture Proceedings Article

In: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022.

Links | BibTeX

2319.

Xindong Zhang and, Hui Zeng; Zhang, Lei

Efficient Hardware-aware Neural Architecture Search for Image Super-resolution on Mobile Device Proceedings Article

In: ACCV 2022, 2022.

Links | BibTeX

2318.

Tang, Yi; Iwaguchi, Takafumi; Kawasaki, Hiroshi; Sagawa, Ryusuke; Furukawa, Ryo

AutoEnhancer: Transformer on U-Net Architecture search for Underwater Image Enhancement Proceedings Article

In: ACCV 2022, 2022.

Links | BibTeX

2317.

Liu, Jia

Evolving deep neural networks for robust learning PhD Thesis

2022.

Links | BibTeX

2316.

Lu, Zhichao; Cheng, Ran; Huang, Shihua; Zhang, Haoming; Qiu, Changxiao; Yang, Fan

Surrogate-assisted Multiobjective Neural Architecture Search for Real-time Semantic Segmentation Technical Manual

2022.

Links | BibTeX

2315.

Yang, Zhao; Sun, Qingshuang

Toward efficient neural architecture search with dynamic mapping-adaptive sampling for resource-limited edge device Proceedings Article

In: Neural Computing and Applications , 2022.

Links | BibTeX

2314.

Ning, Xuefei; Zhou, Zixuan; Zhao, Junbo; Zhao, Tianchen; Deng, Yiping; Tang, Changcheng; Liang, Shuang; Yang, Huazhong; Wang, Yu

TA-GATES: An Encoding Scheme for Neural Network Architectures Proceedings Article

In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.

Links | BibTeX

2313.

Nalluri, Sravani; Sasikala, R.

A deep neural architecture for SOTA pneumonia detection from chest X-rays Proceedings Article

In: International Journal of System Assurance Engineering and Management , 2022.

Links | BibTeX

2312.

Park, Jae Bok; Lee, Kyung Hee; Kwak, Ji Young; Cho, Chang Sik

Deployment Framework Design Techniques for Optimized Neural Network Applicati Technical Manual

2022.

Links | BibTeX

2311.

Lu, Bingqian

Towards Ultra-Efficient Machine Learning for Edge Inference PhD Thesis

2022.

Links | BibTeX

2310.

Lu, Zexin; Xia, Wenjun; Huang, Yongqiang; Hou, Mingzheng; Chen, Hu; Zhou, Jiliu; Shan, Hongming; Zhang, Yi

M<sup>3</sup>NAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising Journal Article

In: IEEE transactions on medical imaging, vol. PP, 2022, ISSN: 0278-0062.

Abstract | Links | BibTeX

@article{PMID:36327187,

title = {M^{3}NAS: Multi-Scale and Multi-Level Memory-Efficient Neural Architecture Search for Low-Dose CT Denoising},

author = {Zexin Lu and Wenjun Xia and Yongqiang Huang and Mingzheng Hou and Hu Chen and Jiliu Zhou and Hongming Shan and Yi Zhang},

url = {https://doi.org/10.1109/TMI.2022.3219286},

doi = {10.1109/tmi.2022.3219286},

issn = {0278-0062},

year  = {2022},

date = {2022-11-01},

urldate = {2022-11-01},

journal = {IEEE transactions on medical imaging},

volume = {PP},

abstract = {Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to public health. However, the reconstructed images from dose-reduced CT or low-dose CT (LDCT) suffer from severe noise which compromises the subsequent diagnosis and analysis. Recently, convolutional neural networks have achieved promising results in removing noise from LDCT images. The network architectures that are used are either handcrafted or built on top of conventional networks such as ResNet and U-Net. Recent advances in neural network architecture search (NAS) have shown that the network architecture has a dramatic effect on the model performance. This indicates that current network architectures for LDCT may be suboptimal. Therefore, in this paper, we make the first attempt to apply NAS to LDCT and propose a multi-scale and multi-level memory-efficient NAS for LDCT denoising, termed M^{3}NAS. On the one hand, the proposed M^{3}NAS fuses features extracted by different scale cells to capture multi-scale image structural details. On the other hand, the proposed M^{3}NAS can search a hybrid cell- and network-level structure for better performance. In addition, M^{3}NAS can effectively reduce the number of model parameters and increase the speed of inference. Extensive experimental results on two different datasets demonstrate that the proposed M^{3}NAS can achieve better performance and fewer parameters than several state-of-the-art methods. In addition, we also validate the effectiveness of the multi-scale and multi-level architecture for LDCT denoising, and present further analysis for different configurations of super-net.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Close

2309.

Zhang, Yang; Zong, Ruohan; Kou, Ziyi; Shang, Lanyu; Wang, Dong

CrowdNAS: A Crowd-Guided Neural Architecture Searching Approach to Disaster Damage Assessment Journal Article

In: Proc. ACM Hum.-Comput. Interact., vol. 6, no. CSCW2, 2022.

Abstract | Links | BibTeX

@article{10.1145/3555179,

title = {CrowdNAS: A Crowd-Guided Neural Architecture Searching Approach to Disaster Damage Assessment},

author = {Yang Zhang and Ruohan Zong and Ziyi Kou and Lanyu Shang and Dong Wang},

url = {https://doi.org/10.1145/3555179},

doi = {10.1145/3555179},

year  = {2022},

date = {2022-11-01},

urldate = {2022-11-01},

journal = {Proc. ACM Hum.-Comput. Interact.},

volume = {6},

number = {CSCW2},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Disaster damage assessment (DDA) has emerged as an important application in disaster response and management, which aims to assess the damage severity of an affected area by leveraging AI (e.g., deep learning) techniques to examine the imagery data posted on social media during a disaster event. In this paper, we focus on a crowd-guided neural architecture searching (NAS) problem in DDA applications. Our goal is to leverage human intelligence from crowdsourcing systems to guide the discovery of the optimal neural network architecture in the design space to achieve the desirable damage assessment performance. Our work is motivated by the limitation that the deep neural network architectures in current DDA solutions are mainly designed by AI experts, which is known to be both time-consuming and error-prone. Two critical technical challenges exist in solving our problem: i) it is challenging to design a manageable NAS space for crowd-based solutions; ii) it is non-trivial to transfer the imperfect crowd knowledge to effective decisions in identifying the optimal neural network architecture of a DDA application. To address the above challenges, we develop CrowdNAS, a crowd-guided NAS framework that develops novel techniques inspired by AI, crowdsourcing, and estimation theory to address the NAS problem. The evaluation results from two real-world DDA applications show that CrowdNAS consistently outperforms the state-of-the-art AI-only, crowd-AI, and NAS baselines by achieving the highest classification accuracy in the damage assessment while maintaining a low computational cost under various evaluation scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Close

2308.

Jin, Charles; Phothilimthana, Phitchaya Mangpo; Roy, Sudip

Neural Architecture Search Using Property Guided Synthesis Journal Article

In: Proc. ACM Program. Lang., vol. 6, no. OOPSLA2, 2022.

Abstract | Links | BibTeX

@article{10.1145/3563329,

title = {Neural Architecture Search Using Property Guided Synthesis},

author = {Charles Jin and Phitchaya Mangpo Phothilimthana and Sudip Roy},

url = {https://doi.org/10.1145/3563329},

doi = {10.1145/3563329},

year  = {2022},

date = {2022-10-01},

urldate = {2022-10-01},

journal = {Proc. ACM Program. Lang.},

volume = {6},

number = {OOPSLA2},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Neural architecture search (NAS) has become an increasingly important tool within the deep learning community in recent years, yielding many practical advancements in the design of deep neural network architectures. However, most existing approaches operate within highly structured design spaces, and hence (1) explore only a small fraction of the full search space of neural architectures while also (2) requiring significant manual effort from domain experts. In this work, we develop techniques that enable efficient NAS in a significantly larger design space. In particular, we propose to perform NAS in an abstract search space of program properties. Our key insights are as follows: (1) an abstract search space can be significantly smaller than the original search space, and (2) architectures with similar program properties should also have similar performance; thus, we can search more efficiently in the abstract search space. To enable this approach, we also introduce a novel efficient synthesis procedure, which performs the role of concretizing a set of promising program properties into a satisfying neural architecture. We implement our approach, αNAS, within an evolutionary framework, where the mutations are guided by the program properties. Starting with a ResNet-34 model, αNAS produces a model with slightly improved accuracy on CIFAR-10 but 96% fewer parameters. On ImageNet, αNAS is able to improve over Vision Transformer (30% fewer FLOPS and parameters), ResNet-50 (23% fewer FLOPS, 14% fewer parameters), and EfficientNet (7% fewer FLOPS and parameters) without any degradation in accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}