AutoML | Literature on Neural Architecture Search

2374.

Tu, Chongjun; Ye, Peng; Lin, Weihao; Ye, Hancheng; Yu, Chong; Chen, Tao; Li, Baopu; Ouyang, Wanli

Efficient Architecture Search via Bi-level Data Pruning Technical Report

2023.

2373.

Wu, Xuan; Wang, Di; Chen, Huanhuan; Yan, Lele; Xiao, Yubin; Miao, Chunyan; Ge, Hongwei; Xu, Dong; Liang, Yanchun; Wang, Kangping; Wu, Chunguo; Zhou, You

Neural Architecture Search for Text Classification With Limited Computing Resources Using Efficient Cartesian Genetic Programming Journal Article

In: IEEE Transactions on Evolutionary Computation, pp. 1-1, 2023.

Links | BibTeX

2372.

Wen, Yingpeng; Yu, Weijiang; Zheng, Fudan; Huang, Dan; Xiao, Nong

AdaNAS: Adaptively Post-processing with Self-supervised Neural Architecture Search for Ensemble Rainfall Forecasts Technical Report

2023.

BibTeX

2371.

Sun, Haiyang; Lian, Zheng; Sun, Licai; Liu, Bin; Tao, Jianhua

RMNAS: A Multimodal Neural Architecture Search Framework For Robust Multimodal Sentiment Analysis Technical Report

2023.

Links | BibTeX

2370.

Zhou, Xun; Wang, Zhenkun; Feng, Liang; Liu, Songbai; Wong, Ka-Chun; Tan, Kay Chen

Towards Evolutionary Multi-Task Convolutional Neural Architecture Search Journal Article

In: IEEE Transactions on Evolutionary Computation, pp. 1-1, 2023.

Links | BibTeX

2369.

Leal, Daivid V.; Araujo, Israel F.; Silva, Adenilton J.

Training and meta-training an ensemble of binary neural networks with quantum computing Journal Article

In: Neurocomputing, pp. 127169, 2023, ISSN: 0925-2312.

Abstract | Links | BibTeX

2368.

Khare, Alind; Garg, Dhruv; Kalra, Sukrit; Grandhi, Snigdha; Stoica, Ion; Tumanov, Alexey

SuperServe: Fine-Grained Inference Serving for Unpredictable Workloads Technical Report

2023.

Links | BibTeX

2367.

Zhang, Mengxuan; Liu, Long; Lei, Zhikun; Ma, Kun; Feng, Jie; Liu, Zhao; Jiao, Licheng

Multi-Scale Spatial-Channel Transformer Architecture Search for Remote Sensing Image Change Detection Journal Article

In: IEEE Geoscience and Remote Sensing Letters, pp. 1-1, 2023.

Links | BibTeX

2366.

Zhang, Fangyu; Pan, Yi; Wei, Yanjie; Liu, Jin; Lin, Zhenli

A Heuristic Sparsification Method to Improve the Performance of Neural Networks on Autism Spectrum Disorder Identification Tasks Proceedings Article

In: 2023 13th International Conference on Information Science and Technology (ICIST), pp. 1-8, 2023.

Links | BibTeX

2365.

Gong, Cheng; Nan, Yang; Pang, Lie Meng; Ishibuchi, Hisao; Zhang, Qingfu

Examination of the Multimodal Nature of Multi-Objective Neural Architecture Search Proceedings Article

In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1821-1828, 2023.

Links | BibTeX

2364.

Londt, Trevor; Gao, Xiaoying; Andreae, Peter; Mei, Yi

A Two-Stage Hybrid GA-Cellular Encoding Approach to Neural Architecture Search Proceedings Article

In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1814-1820, 2023.

Links | BibTeX

2363.

Meng, Lingtong; Chen, Yuting

DFairNAS: A Dataflow Fairness Approach to Training NAS Neural Networks Proceedings Article

In: 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1-6, 2023.

Links | BibTeX

2362.

Ser, Javier Del; Andrés, Alain; Bilbao, Miren Nekane; Laña, Ibai; Lobo, Jesus L.

Evolutionary Multi-Objective Quantization of Randomization-Based Neural Networks Proceedings Article

In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1346-1351, 2023.

Links | BibTeX

2361.

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.

Links | BibTeX

2360.

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

2359.

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

2358.

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

2357.

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

2356.

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

2355.

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

2354.

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

2353.

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

2352.

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

2351.

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

2350.

Mandal, Murari; Meedimale, Yashwanth Reddy; Reddy, M. Satish Kumar; Vipparthi, Santosh Kumar

Neural Architecture Search for Image Dehazing Journal Article

In: IEEE Transactions on Artificial Intelligence, vol. 4, no. 5, pp. 1337-1347, 2023.

Links | BibTeX

2349.

Yan, Shen

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

2022.

Links | BibTeX

2348.

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

2347.

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

2346.

Men, Yanger; Li, Jingtao

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

2022.

Links | BibTeX

2345.

Fan, Liang; Wang, Handing

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

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

Links | BibTeX

2344.

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

2343.

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

2342.

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}

}

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2341.

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}

}

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2340.

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}

}