Maintained by Difan Deng and Marius Lindauer.
The following list considers papers related to neural architecture search. It is by no means complete. If you miss a paper on the list, please let us know.
Please note that although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization. We would therefore like to share some best practices for empirical evaluations of NAS methods, which we believe will facilitate sustained and measurable progress in the field. If you are interested in a teaser, please read our blog post or directly jump to our checklist.
Transformers have gained increasing popularity in different domains. For a comprehensive list of papers focusing on Neural Architecture Search for Transformer-Based spaces, the awesome-transformer-search repo is all you need.
0000
28.
Cheng, Hsin-Pai
Efficient and Generalizable Neural Architecture Search for Visual Recognition PhD Thesis
0000.
@phdthesis{ChengPhD2021,
title = {Efficient and Generalizable Neural Architecture Search for Visual Recognition},
author = {Hsin-Pai Cheng},
url = {https://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/23808/Cheng_duke_0066D_16412.pdf},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
27.
Zhu, Xunyu; Li, Jian; Liu, Yong; Liao, Jun; Wang, Weiping
Operation-level Progressive Differentiable Architecture Search Technical Report
0000.
@techreport{ZhuDARTS2021,
title = {Operation-level Progressive Differentiable Architecture Search},
author = {Xunyu Zhu and Jian Li and Yong Liu and Jun Liao and Weiping Wang},
url = {https://gsai.ruc.edu.cn/uploads/20210924/52c916158c2b3d29015ca71d85484c27.pdf},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
26.
Huang, Sian-Yao; Chu, Wei-Ta
OSNASLib: One-Shot NAS Library Inproceedings
In: ICCV 2021 Workshop on Neural Architectures: Past, Present and Future, 0000.
@inproceedings{HuangISNASLib2021,
title = {OSNASLib: One-Shot NAS Library},
author = {Sian-Yao Huang and Wei-Ta Chu},
url = {https://neural-architecture-ppf.github.io/papers/00010.pdf},
booktitle = {ICCV 2021 Workshop on Neural Architectures: Past, Present and Future},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
25.
Madhu, G.; Bharadwaj, B. Lalith; Boddeda, Rohit; Vardhan, Sai; Kautish, K. Sandeep; Alnowibet, Khalid; Alrasheedi, Adel F.; Mohamed, Ali Wagdy
Deep Stacked Ensemble Learning Model for COVID-19 Classification Technical Report
0000.
@techreport{Madhu2021,
title = {Deep Stacked Ensemble Learning Model for COVID-19 Classification},
author = {G. Madhu and B. Lalith Bharadwaj and Rohit Boddeda and Sai Vardhan and K. Sandeep Kautish and Khalid Alnowibet and Adel F. Alrasheedi and Ali Wagdy Mohamed},
url = {https://www.researchgate.net/profile/B-Lalith-Bharadwaj/publication/355180470_Deep_Stacked_Ensemble_Learning_Model_for_COVID-19_Classification/links/6164cb470bf51d4817768880/Deep-Stacked-Ensemble-Learning-Model-for-COVID-19-Classification.pdf},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
24.
Kandukuri, Nikhil; Sakhtivel, Sangeetha; Xie, Pengtao
Neural Architecture Search For Skin Cancer Detection Technical Report
0000.
@techreport{Kandukuri2021,
title = {Neural Architecture Search For Skin Cancer Detection},
author = {Nikhil Kandukuri and Sangeetha Sakhtivel and Pengtao Xie},
url = {https://assets.researchsquare.com/files/rs-953342/v1_covered.pdf?c=1633976839},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
23.
Jiang, Yingying; Gan, Zhuoxin; Lin, Ke; A, Yong
AttNAS: Searching Attentions for Lightweight Semantic Segmentation Inproceedings
In: British Machine Vision Conference (BMVC) 2021, 0000.
@inproceedings{Jiang2021,
title = {AttNAS: Searching Attentions for Lightweight Semantic Segmentation},
author = {Yingying Jiang and Zhuoxin Gan and Ke Lin and Yong A},
url = {https://www.bmvc2021-virtualconference.com/assets/papers/0575.pdf},
booktitle = {British Machine Vision Conference (BMVC) 2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
22.
Loni, Mohammad; Mousavi, Hamid; Riazati, Mohammad; Daneshtalab, Masoud; Sjödin, Mikael
TAS : Ternarized Neural Architecture Search for Resource-Constrained Edge Devices Inproceedings
In: Design, Automation and Test in Europe ConferenceDesign, Automation and Test in Europe Conference (DATE) 2022, ANTWERP, BELGIUM :, 0000.
@inproceedings{Loni1620831,
title = {TAS : Ternarized Neural Architecture Search for Resource-Constrained Edge Devices},
author = {Mohammad Loni and Hamid Mousavi and Mohammad Riazati and Masoud Daneshtalab and Mikael Sjödin},
url = {https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1620831&dswid=-1720},
booktitle = {Design, Automation and Test in Europe ConferenceDesign, Automation and Test in Europe Conference (DATE) 2022, ANTWERP, BELGIUM :},
institution = {Shahid Bahonar University of Kerman, Iran},
abstract = {Ternary Neural Networks (TNNs) compress network weights and activation functions into 2-bit representation resulting in remarkable network compression and energy efficiency. However, there remains a significant gap in accuracy between TNNs and full-precision counterparts. Recent advances in Neural Architectures Search (NAS) promise opportunities in automated optimization for various deep learning tasks. Unfortunately, this area is unexplored for optimizing TNNs. This paper proposes TAS, a framework that drastically reduces the accuracy gap between TNNs and their full-precision counterparts by integrating quantization into the network design. We experienced that directly applying NAS to the ternary domain provides accuracy degradation as the search settings are customized for full-precision networks. To address this problem, we propose (i) a new cell template for ternary networks with maximum gradient propagation; and (ii) a novel learnable quantizer that adaptively relaxes the ternarization mechanism from the distribution of the weights and activation functions. Experimental results reveal that TAS delivers 2.64% higher accuracy and 2.8x memory saving over competing methods with the same bit-width resolution on the CIFAR-10 dataset. These results suggest that TAS is an effective method that paves the way for the efficient design of the next generation of quantized neural networks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ternary Neural Networks (TNNs) compress network weights and activation functions into 2-bit representation resulting in remarkable network compression and energy efficiency. However, there remains a significant gap in accuracy between TNNs and full-precision counterparts. Recent advances in Neural Architectures Search (NAS) promise opportunities in automated optimization for various deep learning tasks. Unfortunately, this area is unexplored for optimizing TNNs. This paper proposes TAS, a framework that drastically reduces the accuracy gap between TNNs and their full-precision counterparts by integrating quantization into the network design. We experienced that directly applying NAS to the ternary domain provides accuracy degradation as the search settings are customized for full-precision networks. To address this problem, we propose (i) a new cell template for ternary networks with maximum gradient propagation; and (ii) a novel learnable quantizer that adaptively relaxes the ternarization mechanism from the distribution of the weights and activation functions. Experimental results reveal that TAS delivers 2.64% higher accuracy and 2.8x memory saving over competing methods with the same bit-width resolution on the CIFAR-10 dataset. These results suggest that TAS is an effective method that paves the way for the efficient design of the next generation of quantized neural networks.
21.
Ma, Zhiyuan; Yu, Wenting; Zhang, Peng; Huang, Zhi; Lin, Anni; Xia, Yan
LPI Radar Waveform Recognition Based on Neural Architecture Search Journal Article
In: Computational Intelligence and Neuroscience, vol. 2022, 0000.
@article{Ma2022,
title = {LPI Radar Waveform Recognition Based on Neural Architecture Search},
author = {Zhiyuan Ma and Wenting Yu and Peng Zhang and Zhi Huang and Anni Lin and Yan Xia},
url = {https://doi.org/10.1155/2022/4628481},
journal = {Computational Intelligence and Neuroscience},
volume = {2022},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
20.
Salam, Hanan; Manoranjan, Viswonathan; Jian, Jiang; Celiktutan, Oya
Learning Personalised Models for Automatic Self-Reported Personality Recognition Technical Report
0000.
@techreport{Salam2022,
title = {Learning Personalised Models for Automatic Self-Reported Personality Recognition},
author = {Hanan Salam and Viswonathan Manoranjan and Jiang Jian and Oya Celiktutan},
url = {https://nyuscholars.nyu.edu/ws/files/139000086/Proceedings_ICCV_2021_Understanding_Social_Behavior_in_Dyadic_and_Small_Group_Interactions_Challenge_8.pdf},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
19.
Dhanaraja, Mayur; Dob, Huyen; Nairb, Dinesh; Xub, Cong
Leveraging Tensor Methods in Neural Architecture Search for the automatic development of lightweight Convolutional Neural Networks Journal Article
In: 0000.
@article{dhanarajaleveraging,
title = {Leveraging Tensor Methods in Neural Architecture Search for the automatic development of lightweight Convolutional Neural Networks},
author = {Mayur Dhanaraja and Huyen Dob and Dinesh Nairb and Cong Xub},
url = {https://assets.amazon.science/34/29/484392a6450b8af9b7646fe6db60/leveraging-tensor-methods-in-neural-architecture-search-for-the-automatic-development-of-lightweight-convolutional-neural-networks.pdf},
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18.
Yang, Chengrun; Bender, Gabriel; Liu, Hanxiao; Kindermans, Pieter-Jan; Udell, Madeleine; Lu, Yifeng; Le, Quoc; Huang, Da
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets Technical Report
0000.
@techreport{https://doi.org/10.48550/arxiv.2204.07615,
title = {TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets},
author = {Chengrun Yang and Gabriel Bender and Hanxiao Liu and Pieter-Jan Kindermans and Madeleine Udell and Yifeng Lu and Quoc Le and Da Huang},
url = {https://arxiv.org/abs/2204.07615},
doi = {10.48550/ARXIV.2204.07615},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
17.
Hao, Yao; Zhang, Xizhe; Wang, Jie; Zhao, Tianyu; Sun, Baozhou
Improvement of IMRT QA prediction using imaging-based neural architecture search Journal Article
In: Medical Physics, vol. n/a, no. n/a, 0000.
@article{https://doi.org/10.1002/mp.15694,
title = {Improvement of IMRT QA prediction using imaging-based neural architecture search},
author = {Yao Hao and Xizhe Zhang and Jie Wang and Tianyu Zhao and Baozhou Sun},
url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.15694},
doi = {https://doi.org/10.1002/mp.15694},
journal = {Medical Physics},
volume = {n/a},
number = {n/a},
abstract = {Abstract Purpose Machine learning (ML) has been used to predict the gamma passing rate (GPR) of intensity-modulated radiation therapy (IMRT) QA results. In this work, we applied a novel neural architecture search to automatically tune and search for the best deep neural networks instead of using hand-designed deep learning architectures. Method and materials One hundred and eighty-two IMRT plans were created and delivered with portal dosimetry. A total of 1497 fields for multiple treatment sites were delivered and measured by portal imagers. Gamma criteria of 2%/2 mm with a 5% threshold were used. Fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). Auto-Keras was implemented to search for the best CNN architecture for fluence image regression. The network morphism was adopted in the searching process, in which the base models were ResNet and DenseNet. The performance of this CNN approach was compared with tree-based ML models previously developed for this application, using the same dataset. Results The deep-learning-based approach had 98.3% of predictions within 3% of the measured 2%/2-mm GPRs with a maximum error of 3.1% and a mean absolute error of less than 1%. Our results show that this novel architecture search approach achieves comparable performance to the machine-learning-based approaches with handcrafted features. Conclusions We implemented a novel CNN model using imaging-based neural architecture for IMRT QA prediction. The imaging-based deep-learning method does not require a manual extraction of relevant features and is able to automatically select the best network architecture.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Purpose Machine learning (ML) has been used to predict the gamma passing rate (GPR) of intensity-modulated radiation therapy (IMRT) QA results. In this work, we applied a novel neural architecture search to automatically tune and search for the best deep neural networks instead of using hand-designed deep learning architectures. Method and materials One hundred and eighty-two IMRT plans were created and delivered with portal dosimetry. A total of 1497 fields for multiple treatment sites were delivered and measured by portal imagers. Gamma criteria of 2%/2 mm with a 5% threshold were used. Fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). Auto-Keras was implemented to search for the best CNN architecture for fluence image regression. The network morphism was adopted in the searching process, in which the base models were ResNet and DenseNet. The performance of this CNN approach was compared with tree-based ML models previously developed for this application, using the same dataset. Results The deep-learning-based approach had 98.3% of predictions within 3% of the measured 2%/2-mm GPRs with a maximum error of 3.1% and a mean absolute error of less than 1%. Our results show that this novel architecture search approach achieves comparable performance to the machine-learning-based approaches with handcrafted features. Conclusions We implemented a novel CNN model using imaging-based neural architecture for IMRT QA prediction. The imaging-based deep-learning method does not require a manual extraction of relevant features and is able to automatically select the best network architecture.
16.
Zhai, Qihang; Li, Yan; Zhang, Zilin; Li, Yunjie; Wang, Shafei
Adaptive feature extraction and fine-grained modulation recognition of multi-function radar under small sample conditions Journal Article
In: IET Radar, Sonar & Navigation, vol. n/a, no. n/a, 0000.
@article{https://doi.org/10.1049/rsn2.12273,
title = {Adaptive feature extraction and fine-grained modulation recognition of multi-function radar under small sample conditions},
author = {Qihang Zhai and Yan Li and Zilin Zhang and Yunjie Li and Shafei Wang},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/rsn2.12273},
doi = {https://doi.org/10.1049/rsn2.12273},
journal = {IET Radar, Sonar & Navigation},
volume = {n/a},
number = {n/a},
abstract = {Abstract Multi-function radars (MFRs) are sophisticated sensors with fine-grained modes, which modify their modulation types and parameters range generating various signals to fulfil different tasks, such as surveillance and tracking. In electromagnetic reconnaissance, recognition of MFR fine-grained modes can provide a basis for analysing strategies and reaction. With the limit of real applications, it is hard to obtain a large number of labelled samples for existing methods to learn the difference between categories. Therefore, it is essential to develop new methods to extract general knowledge of MFRs and identify modes with only a few samples. This paper proposes a few-shot learning (FSL) framework based on efficient neural architecture search (ENAS) with high robustness and portability, which designs a suitable network structure automated and quickly adapts to new environments. The experimental results show that the proposed method can still achieve excellent fine-grained modulation recognition performance (92.6%) under the condition of -6 dB signal-to-noise ratio (SNR), even when each class only provides one fixed-duration signal sample. The robustness is also verified under different conditions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Multi-function radars (MFRs) are sophisticated sensors with fine-grained modes, which modify their modulation types and parameters range generating various signals to fulfil different tasks, such as surveillance and tracking. In electromagnetic reconnaissance, recognition of MFR fine-grained modes can provide a basis for analysing strategies and reaction. With the limit of real applications, it is hard to obtain a large number of labelled samples for existing methods to learn the difference between categories. Therefore, it is essential to develop new methods to extract general knowledge of MFRs and identify modes with only a few samples. This paper proposes a few-shot learning (FSL) framework based on efficient neural architecture search (ENAS) with high robustness and portability, which designs a suitable network structure automated and quickly adapts to new environments. The experimental results show that the proposed method can still achieve excellent fine-grained modulation recognition performance (92.6%) under the condition of -6 dB signal-to-noise ratio (SNR), even when each class only provides one fixed-duration signal sample. The robustness is also verified under different conditions.
15.
Wang, Ye-Qun; Li, Jian-Yu; Chen, Chun-Hua; Zhang, Jun; Zhan, Zhi-Hui
In: CAAI Transactions on Intelligence Technology, vol. n/a, no. n/a, 0000.
@article{https://doi.org/10.1049/cit2.12106,
title = {Scale adaptive fitness evaluation-based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning},
author = {Ye-Qun Wang and Jian-Yu Li and Chun-Hua Chen and Jun Zhang and Zhi-Hui Zhan},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/cit2.12106},
doi = {https://doi.org/10.1049/cit2.12106},
journal = {CAAI Transactions on Intelligence Technology},
volume = {n/a},
number = {n/a},
abstract = {Abstract Research into automatically searching for an optimal neural network (NN) by optimisation algorithms is a significant research topic in deep learning and artificial intelligence. However, this is still challenging due to two issues: Both the hyperparameter and architecture should be optimised and the optimisation process is computationally expensive. To tackle these two issues, this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light-weight scale-adaptive fitness evaluation-based particle swarm optimisation (SAFE-PSO) approach. Firstly, the SAFE-PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN. Secondly, the computational cost can be reduced by using multi-scale accuracy evaluation methods to evaluate candidates. Thirdly, a stagnation-based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost. The SAFE-PSO algorithm is tested on two widely used datasets: The 10-category (i.e., CIFAR10) and the 100−category (i.e., CIFAR100). The experimental results show that SAFE-PSO is very effective and efficient, which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Abstract Research into automatically searching for an optimal neural network (NN) by optimisation algorithms is a significant research topic in deep learning and artificial intelligence. However, this is still challenging due to two issues: Both the hyperparameter and architecture should be optimised and the optimisation process is computationally expensive. To tackle these two issues, this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light-weight scale-adaptive fitness evaluation-based particle swarm optimisation (SAFE-PSO) approach. Firstly, the SAFE-PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best NN. Secondly, the computational cost can be reduced by using multi-scale accuracy evaluation methods to evaluate candidates. Thirdly, a stagnation-based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational cost. The SAFE-PSO algorithm is tested on two widely used datasets: The 10-category (i.e., CIFAR10) and the 100−category (i.e., CIFAR100). The experimental results show that SAFE-PSO is very effective and efficient, which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.
14.
Hu, Weifei; jinyi Shao,; Jiao, Qing; Wang, Chuxuan; Cheng, Jin; Liu, Zhenyu; Tan, Jianrong
A new differentiable architecture search method for optimizing convolutional neural networks in the digital twin of intelligent robotic grasping Journal Article
In: Journal of Intelligent Manufacturing, 0000.
@article{nokey,
title = {A new differentiable architecture search method for optimizing convolutional neural networks in the digital twin of intelligent robotic grasping},
author = {Weifei Hu and jinyi Shao and Qing Jiao and Chuxuan Wang and Jin Cheng and Zhenyu Liu and Jianrong Tan},
url = {https://link.springer.com/article/10.1007/s10845-022-01971-8},
journal = { Journal of Intelligent Manufacturing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
13.
Xue, Fanghui
Relaxation and Optimization for Automated Learning of Neural Network Architectures DISSERTATION PhD Thesis
0000.
@phdthesis{XuePHD2022,
title = {Relaxation and Optimization for Automated Learning of Neural Network Architectures DISSERTATION},
author = {Fanghui Xue},
url = {https://escholarship.org/content/qt3wt239sm/qt3wt239sm.pdf},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
12.
Mokhtari, Nassim; Nédélec, Alexis; Gilles, Marlene; Loor, Pierre De
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free Evaluation Technical Report
0000.
@techreport{mokhtariimproving,
title = {Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free Evaluation},
author = {Nassim Mokhtari and Alexis Nédélec and Marlene Gilles and Pierre De Loor},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
11.
Duggal, Rahul
0000.
@phdthesis{DuggalPhD22,
title = {Robust Efficient Edge AI: New Principles and Frameworks for Empowering Artifical Intelligence on Edge Device ON EDGE DEVICES},
author = {Rahul Duggal},
url = {https://smartech.gatech.edu/bitstream/handle/1853/67315/DUGGAL-DISSERTATION-2022.pdf?sequence=1},
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pubstate = {published},
tppubtype = {phdthesis}
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10.
Wen, Hao; Kang, Jingsu
Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram Technical Report
0000.
@techreport{WenTS22,
title = {Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram},
author = {Hao Wen and Jingsu Kang},
url = {https://cinc.org/2022/Program/accepted/130_Preprint.pdf},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
9.
Loni, Mohammad
Efficient Design of Scalable Deep Neural Networks for Resource-Constrained Edge Devices PhD Thesis
0000.
@phdthesis{LoniPhD,
title = {Efficient Design of Scalable Deep Neural Networks for Resource-Constrained Edge Devices},
author = {Mohammad Loni},
url = {https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1695852&dswid=-3791},
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tppubtype = {phdthesis}
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8.
Wenfeng Feng and, Xin Zhang; Song, Qiushuang; Sun, Guoying
Incoherence of Deep Isotropic Neural Networks increase their performance on Image Classification Technical Manual
0000.
@manual{nokey,
title = {Incoherence of Deep Isotropic Neural Networks increase their performance on Image Classification},
author = { Wenfeng Feng and, Xin Zhang and Qiushuang Song and Guoying Sun
},
url = {https://www.preprints.org/manuscript/202210.0092/v1},
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pubstate = {published},
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7.
Malkova, Aleksandra; Amini, Massih-Reza; Denis, Benoit; Villien, Christophe
Radio Map Reconstruction with Deep Neural Networks in a Weakly Labeled Learning Context with use of Heterogeneous Side Information Technical Manual
0000.
@manual{Malkova2022,
title = {Radio Map Reconstruction with Deep Neural Networks in a Weakly Labeled Learning Context with use of Heterogeneous Side Information},
author = {Aleksandra Malkova and Massih-Reza Amini and Benoit Denis and Christophe Villien},
url = {https://hal.archives-ouvertes.fr/hal-03823629/document},
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pubstate = {published},
tppubtype = {manual}
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6.
Aboalam, Kawther; Neuswirth, Christoph; Pernau, Florian; Schiebel, Stefan; Spaethe, Fabian; Strohrmann, Manfred
Image Processing and Neural Network Optimization Methods for Automatic Visual Inspection Technical Manual
0000.
@manual{Aboalam2022,
title = {Image Processing and Neural Network Optimization Methods for Automatic Visual Inspection},
author = {Kawther Aboalam and Christoph Neuswirth and Florian Pernau and Stefan Schiebel and Fabian Spaethe and Manfred Strohrmann},
url = {https://www.researchgate.net/profile/Christoph-Reich/publication/364343172_Artificial_Intelligence_--_Applications_in_Medicine_and_Manufacturing_--_The_Upper_Rhine_Artificial_Intelligence_Symposium_UR-AI_2022/links/634cfa3476e39959d6c8bfb2/Artificial-Intelligence--Applications-in-Medicine-and-Manufacturing--The-Upper-Rhine-Artificial-Intelligence-Symposium-UR-AI-2022.pdf#page=33},
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5.
Mishra, Vidyanand; Kane, Lalit
A survey of designing convolutional neural network using evolutionary algorithms Journal Article
In: Artificial Intelligence Review, 0000.
@article{Mishra-AIR2022,
title = {A survey of designing convolutional neural network using evolutionary algorithms},
author = {Vidyanand Mishra and Lalit Kane
},
url = {https://link.springer.com/article/10.1007/s10462-022-10303-4},
journal = { Artificial Intelligence Review},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
4.
Sun, Yanan; Yen, Gary G.; Zhang, Mengjie
Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and recent AdvanceYa Book
0000.
@book{SunEDA22,
title = {Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and recent AdvanceYa},
author = {Yanan Sun and Gary G. Yen and Mengjie Zhang},
url = {https://books.google.de/books?hl=de&lr=&id=2RWbEAAAQBAJ&oi=fnd&pg=PR5&dq=%22neural+architecture+search%22&ots=yjnrR-vqyW&sig=0KFGVSnhQWTc1sQmWWewvmeuGqw#v=onepage&q=%22neural%20architecture%20search%22&f=false},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
3.
Park, Minje
Proxy Data Generation for Fast and Efficient Neural Architecture Search Journal Article
In: Journal of Electrical Engineering & Technology 2022, 0000.
@article{ParkJEET22,
title = {Proxy Data Generation for Fast and Efficient Neural Architecture Search},
author = {
Minje Park
},
url = {https://link.springer.com/article/10.1007/s42835-022-01321-x},
journal = {Journal of Electrical Engineering & Technology 2022},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2.
Singh, Yeshwant; Biswas, Anupam
In: Expert Systems, vol. n/a, no. n/a, pp. e13241, 0000.
@article{https://doi.org/10.1111/exsy.13241,
title = {Lightweight convolutional neural network architecture design for music genre classification using evolutionary stochastic hyperparameter selection},
author = {Yeshwant Singh and Anupam Biswas},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13241},
doi = {https://doi.org/10.1111/exsy.13241},
journal = {Expert Systems},
volume = {n/a},
number = {n/a},
pages = {e13241},
abstract = {Abstract Convolutional neural networks (CNNs) have succeeded in various domains, including music information retrieval (MIR). Music genre classification (MGC) is one such task in the MIR that has gained attention over the years because of the massive increase in online music content. Accurate indexing and automatic classification of these large volumes of music content require high computational resources, which pose a significant challenge to building a lightweight system. CNNs are a popular deep learning-based choice for building systems for MGC. However, finding an optimal CNN architecture for MGC requires domain knowledge both in CNN architecture design and music. We present MGA-CNN, a genetic algorithm-based approach with a novel stochastic hyperparameter selection for finding an optimal lightweight CNN-based architecture for the MGC task. The proposed approach is unique in automating the CNN architecture design for the MGC task. MGA-CNN is evaluated on three widely used music datasets and compared with seven peer rivals, which include three automatic CNN architecture design approaches and four manually designed popular CNN architectures. The experimental results show that MGA-CNN surpasses the peer approaches in terms of classification accuracy, parameter numbers, and execution time. The optimal architectures generated by MGA-CNN also achieve classification accuracy comparable to the manually designed CNN architectures while spending fewer computing resources.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Convolutional neural networks (CNNs) have succeeded in various domains, including music information retrieval (MIR). Music genre classification (MGC) is one such task in the MIR that has gained attention over the years because of the massive increase in online music content. Accurate indexing and automatic classification of these large volumes of music content require high computational resources, which pose a significant challenge to building a lightweight system. CNNs are a popular deep learning-based choice for building systems for MGC. However, finding an optimal CNN architecture for MGC requires domain knowledge both in CNN architecture design and music. We present MGA-CNN, a genetic algorithm-based approach with a novel stochastic hyperparameter selection for finding an optimal lightweight CNN-based architecture for the MGC task. The proposed approach is unique in automating the CNN architecture design for the MGC task. MGA-CNN is evaluated on three widely used music datasets and compared with seven peer rivals, which include three automatic CNN architecture design approaches and four manually designed popular CNN architectures. The experimental results show that MGA-CNN surpasses the peer approaches in terms of classification accuracy, parameter numbers, and execution time. The optimal architectures generated by MGA-CNN also achieve classification accuracy comparable to the manually designed CNN architectures while spending fewer computing resources.
1.
Gupta, Pritha; Drees, Jan Peter; Hüllermeier, Eyke
Automated Side-Channel Attacks using Black-Box Neural Architecture Search Technical Report
0000.
@techreport{Gupta22,
title = {Automated Side-Channel Attacks using Black-Box Neural Architecture Search},
author = {Pritha Gupta and Jan Peter Drees and Eyke Hüllermeier},
url = {https://eprint.iacr.org/2023/093.pdf},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}