AutoML | Literature on Neural Architecture Search

@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}

}

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2325.

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}

}

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2324.

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}

}

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2323.

Stolle, Kurt; Vogel, Sebastian; van der Sommen, Fons; Sanberg, Willem

Block-Level Surrogate Models for Inference Time Estimation in Hardware-Aware Neural Architecture Search Proceedings Article

In: ECML PKDD 22, 2022.

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2322.

Nowak, Aleksandra I.; Janik, Romuald A.

Discovering wiring patterns influencing neural network performance Proceedings Article

In: ECML PKDD 22, 2022.

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2321.

Franken, Gideon; Singh, Prabhant; Vanschoren, Joaquin

Faster Performance Estimation for NAS with Embedding Proximity Score Proceedings Article

In: ECMLPKDD Workshop on Meta-Knowledge Transfer, 2022.

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2320.

Yuan, Wanqi; Fu, Chenping; Liu, Risheng; Fan, Xin

SSoB: searching a scene-oriented architecture for underwater object detection Journal Article

In: The Visual Computer, 2022.

Links | BibTeX

2319.

Bhowmik, Pritom

Machine Learning in Production: From Experimented ML Model to System Journal Article

In: Journal of Robotics and Automation Research, 2022.

Links | BibTeX

2318.

Li, Yilan; Lu, Yantao; Cui, Helei; Velipasalar, Senem

Improving robustness and efficiency of edge computing models Journal Article

In: Wireless Networks , 2022.

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2317.

Arhore, Edore G.; Yasaee, Mehdi; Dayyani, Iman

Optimisation of convolutional neural network architecture using genetic algorithm for the prediction of adhesively bonded joint strength Journal Article

In: Structural and Multidisciplinary Optimization, 2022.

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2316.

Liu, Yang; Liu, Jing; Li, Yifan

Automatic search of architecture and hyperparameters of graph convolutional networks for node classification Journal Article

In: Applied Intelligence , 2022.

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2315.

Phan, Quan Minh; Luong, Ngoc Hoang

Enhancing multi-objective evolutionary neural architecture search with training-free Pareto local search Journal Article

In: Applied Intelligence, 2022.

Links | BibTeX

2314.

Fadzail, N F; Zali, S Mat; Mid1, E C; Jailani, R

Application of Automated Machine Learning (AutoML) Method in Wind Turbine Fault Detection Journal Article

In: Journal of Physics: Conference Series, 2022.

Links | BibTeX

2313.

ke, Songyu; Song, Li; Bao, Kainan; Pan, Zheyi; Zhang, Junbo; Zheng, Yu

EAST: An Enhanced Automated Machine Learning Library for Spatio-Temporal Forecasting Proceedings Article

In: DeepSpatial ’22, Washington, DC, USA, 2022.

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2312.

Bin, Wang; Hui, Ning

Notebook for PAN at CLEF 2022:Profiling Irony and Stereotype Spreaders on Twitter Proceedings Article

In: CLEF 2022: Conference and Labs of the Evaluation Forum, 2022.

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2311.

Arasakumaran, Umamageswari; Johnson, Shiny Duela; Sara, Dioline; Kothandaraman, Raja

An Enhanced Identification and Classification Algorithm for Plant Leaf Diseases Based on Deep Learning. Journal Article

In: Traitement du Signal, vol. 39, iss. 3, 2022.

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2310.

Chen, Shengbo; Jiang, Kai; Liu, Xianrui; Yang, Kangkang; Lei, Zhou

TGAS-ReID: Efficient architecture search for person re-identification via greedy decisions with topological order Journal Article

In: Applied Intelligence , 2022.

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2309.

Zheng, Kai; Zhao, Haochen; Zhao, Qichang; Wang, Bin; Gao, Xin; Wang, Jianxin

NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks Journal Article

In: Brief Bioinform., 2022.

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2308.

Turner, Jack

Efficient Neural Networks PhD Thesis

2022.

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2307.

Fakieh, Bahjat; Ragab, Mahmoud

Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model Journal Article Forthcoming

In: Computational Intelligence and its Applications in Biomedical Engineering, vol. 2022, Forthcoming.

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2306.

Chen, J.; Gao, J.; Lyu, T.; Oloulade, B.; Hu, X.

AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction Journal Article

In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, no. 01, pp. 1-11, 2022, ISSN: 1557-9964.

Abstract | Links | BibTeX

@article{9864145,

title = {AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction},

author = {J. Chen and J. Gao and T. Lyu and B. Oloulade and X. Hu},

doi = {10.1109/TCBB.2022.3198119},

issn = {1557-9964},

year  = {2022},

date = {2022-08-01},

urldate = {5555-08-01},

journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},

number = {01},

pages = {1-11},

publisher = {IEEE Computer Society},

address = {Los Alamitos, CA, USA},

abstract = {It is significant to comprehend the relationship between metabolic pathway and molecular pathway for synthesizing new molecules, for instance optimizing drug metabolization. In bioinformatics fields, multi-label prediction of metabolic pathways is a typical manner to understand this relationship. Graph neural networks (GNNs) have become an effective method to extract molecular structure&#x0027;s features for multi-label prediction of metabolic pathways. Though GNNs can effectively capture structural features from molecular structure graphs, building a well-performed GNN model for a given molecular structure data set requires the manual design of the GNN architecture and fine-tuning of the hyperparameters, which are time-consuming and rely on expert experience. To address the above challenge, we design an end-to-end automatic molecular structure representation learning framework named AutoMSR that can design the optimal GNN model based on a given molecular structure data set without manual intervention. We propose a multi-seed age evolution (MSAE) search algorithm to identify the optimal GNN architecture from the GNN architecture subspace. For a given molecular structure data set, AutoMSR first uses MSAE to search the GNN architecture, and then it adopts a tree-structured parzen estimator to obtain the best hyperparameters in the hyperparameters subspace. Finally, AutoMSR automatically constructs the optimal GNN model based on the best GNN architecture and hyperparameters to extract the molecular structure features for multi-label metabolic pathway prediction. We test the performance of AutoMSR on the real data set KEGG. The experiment results show that AutoMSR outperforms baseline methods on different multi-label classification evaluation metrics. keywords = computer architecture;task analysis;feature extraction;graph neural networks;prediction algorithms;drugs;architecture},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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2305.

Bechtel, M.; Weng, Q.; Yun, H.

DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems Proceedings Article

In: 2022 IEEE 28th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), pp. 120-127, IEEE Computer Society, Los Alamitos, CA, USA, 2022.

Abstract | Links | BibTeX

2304.

Li, Jolen; Galazis, Christopher; Popov, Illarion; Ovchinnikov, Lev; Vesnin, Sergey; Losev, Alexander; Goryanin, Igor

Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics Technical Report

2022.

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2303.

Jeong, Joonhyun; Yu, Joonsang; Han, Dongyoon; Yoo, YoungJoon

Neural Architecture Search with Loss Flatness-aware Measure Proceedings Article

In: DyNN workshop at the 39th International Conference on Machine Learning, 2022.

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2302.

Peng, Cheng; Li, Yangyang; Shang, Ronghua; Jiao, Licheng

ReCNAS: Resource-Constrained Neural Architecture Search Based on Differentiable Annealing and Dynamic Pruning Journal Article

In: IEEE Trans Neural Netw Learn Syst , 2022.

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2301.

Suchopárová, Gabriela; Neruda, Roman

Graph Embedding for Neural Architecture Search with Input-Output Information Proceedings Article

In: 1st International Conference on, Automated Machine Learning, Late-Breaking Workshop, 2022.

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2300.

van Gastel, Rob; Vanschoren, Joaquin

Regularized Meta-Learning for Neural Architecture Search Proceedings Article

In: 1st International Conference on, Automated Machine Learning, Late-Breaking Workshop, 2022.

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2299.

Chatzimichailidis, Avraam; Zela, Arber; Keuper, Janis; Yang, Yang

GSparsity: Unifying Network Pruning and Neural Architecture Search by Group Sparsity Proceedings Article

In: 1st International Conference on, Automated Machine Learning, Late-Breaking Workshop, 2022.

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2298.

Chen, Panyue; Wang, Rui; Zhao, Ping; Liu, Guanming; Wei, Zhihua

Searching Efficient Dynamic Graph CNN for Point Cloud Processing Proceedings Article

In: 1st International Conference on Automated Machine Learning, Late-Breaking Workshop, 2022.

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2297.

Zhang, Tianning; Kee, Chun Yun; Ang, Yee Sin; LI, Erping; Ang, Lay Kee

Symmetry Enhanced Network Architecture Search for Complex Metasurface Design Journal Article

In: IEEE Access, 2022.

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2296.

Gupta, Abhibha; Sheth, Parth; Xie, Pengtao

Neural architecture search for pneumonia diagnosis from chest X-rays Journal Article

In: Scientific Reports, vol. 12, 2022.

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2295.

Gridin, Ivan

Automated Deep Learning Using Neural Network Intelligence Book

2022, ISBN: 978-1-4842-8148-2.

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2294.

Liu, Jia; Jin, Yaochu

Bi-fidelity Multi-objective Neural Architecture Search for Adversarial Robustness with Surrogate as a Helper-objective Proceedings Article

In: 2022.

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2293.

Fayyazifar, Najmeh

Deep learning and neural architecture search for cardiac arrhythmias classification PhD Thesis

2022.

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2292.

Wen, Long; Wang, You; Li, Xinyu

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis Journal Article

In: Frontiers of Mechanical Engineering, vol. 17, 2022.

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2291.

Parallel and Distributed Methods for Autonomous Design of Artificial Neural Networks PhD Thesis

2022.

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2290.

Sun, Zhenhong; Lin, Ming; Sun, Xiuyu; Tan, Zhiyu; Li, Hao; Jin, Rong

MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection Proceedings Article

In: Chaudhuri, Kamalika; Jegelka, Stefanie; Song, Le; Szepesvari, Csaba; Niu, Gang; Sabato, Sivan (Ed.): Proceedings of the 39th International Conference on Machine Learning, pp. 20810–20826, PMLR, 2022.

Abstract | Links | BibTeX

@inproceedings{pmlr-v162-sun22c,

title = {MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection},

author = {Zhenhong Sun and Ming Lin and Xiuyu Sun and Zhiyu Tan and Hao Li and Rong Jin},

editor = {Kamalika Chaudhuri and Stefanie Jegelka and Le Song and Csaba Szepesvari and Gang Niu and Sivan Sabato},

url = {https://proceedings.mlr.press/v162/sun22c.html},

year  = {2022},

date = {2022-07-01},

urldate = {2022-07-01},

booktitle = {Proceedings of the 39th International Conference on Machine Learning},

volume = {162},

pages = {20810--20826},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

abstract = {In object detection, the detection backbone consumes more than half of the overall inference cost. Recent researches attempt to reduce this cost by optimizing the backbone architecture with the help of Neural Architecture Search (NAS). However, existing NAS methods for object detection require hundreds to thousands of GPU hours of searching, making them impractical in fast-paced research and development. In this work, we propose a novel zero-shot NAS method to address this issue. The proposed method, named MAE-DET, automatically designs efficient detection backbones via the Maximum Entropy Principle without training network parameters, reducing the architecture design cost to nearly zero yet delivering the state-of-the-art (SOTA) performance. Under the hood, MAE-DET maximizes the differential entropy of detection backbones, leading to a better feature extractor for object detection under the same computational budgets. After merely one GPU day of fully automatic design, MAE-DET innovates SOTA detection backbones on multiple detection benchmark datasets with little human intervention. Comparing to ResNet-50 backbone, MAE-DET is $+2.0%$ better in mAP when using the same amount of FLOPs/parameters, and is $1.54$ times faster on NVIDIA V100 at the same mAP. Code and pre-trained models are available here (https://github.com/alibaba/lightweight-neural-architecture-search).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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2289.

Sun, Zhenhong; Lin, Ming; Sun, Xiuyu; Tan, Zhiyu; Li, Hao; Jin, Rong

MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection Proceedings Article

In: Chaudhuri, Kamalika; Jegelka, Stefanie; Song, Le; Szepesvari, Csaba; Niu, Gang; Sabato, Sivan (Ed.): Proceedings of the 39th International Conference on Machine Learning, pp. 20810–20826, PMLR, 2022.

Abstract | Links | BibTeX

@inproceedings{pmlr-v162-sun22cb,

title = {MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection},

author = {Zhenhong Sun and Ming Lin and Xiuyu Sun and Zhiyu Tan and Hao Li and Rong Jin},

editor = {Kamalika Chaudhuri and Stefanie Jegelka and Le Song and Csaba Szepesvari and Gang Niu and Sivan Sabato},

url = {https://proceedings.mlr.press/v162/sun22c.html},

year  = {2022},

date = {2022-07-01},

urldate = {2022-07-01},

booktitle = {Proceedings of the 39th International Conference on Machine Learning},

volume = {162},

pages = {20810--20826},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

abstract = {In object detection, the detection backbone consumes more than half of the overall inference cost. Recent researches attempt to reduce this cost by optimizing the backbone architecture with the help of Neural Architecture Search (NAS). However, existing NAS methods for object detection require hundreds to thousands of GPU hours of searching, making them impractical in fast-paced research and development. In this work, we propose a novel zero-shot NAS method to address this issue. The proposed method, named MAE-DET, automatically designs efficient detection backbones via the Maximum Entropy Principle without training network parameters, reducing the architecture design cost to nearly zero yet delivering the state-of-the-art (SOTA) performance. Under the hood, MAE-DET maximizes the differential entropy of detection backbones, leading to a better feature extractor for object detection under the same computational budgets. After merely one GPU day of fully automatic design, MAE-DET innovates SOTA detection backbones on multiple detection benchmark datasets with little human intervention. Comparing to ResNet-50 backbone, MAE-DET is $+2.0%$ better in mAP when using the same amount of FLOPs/parameters, and is $1.54$ times faster on NVIDIA V100 at the same mAP. Code and pre-trained models are available here (https://github.com/alibaba/lightweight-neural-architecture-search).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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