Literature on Neural Architecture Search

1074.

Sharma, Arun K.; Verma, Nishchal K.

Guided Sampling-based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis Journal Article

In: CoRR, vol. abs/2111.06885, 2021.

1073.

Wu, Bichen; Li, Chaojian; Zhang, Hang; Dai, Xiaoliang; Zhang, Peizhao; Yu, Matthew; Wang, Jialiang; Lin, Yingyan; Vajda, Peter

FBNetV5: Neural Architecture Search for Multiple Tasks in One Run Technical Report

2021.

1072.

He, Haoyu; Liu, Jing; Pan, Zizheng; Cai, Jianfei; Zhang, Jing; Tao, Dacheng; Zhuang, Bohan

Pruning Self-attentions into Convolutional Layers in Single Path Technical Report

2021.

1071.

Jiang, Qian; Zhang, Xiaofan; Chen, Deming; Do, Minh N.; Yeh, Raymond A.

EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search Technical Report

2021.

1070.

Li, Chang; Zhang, Zhongzhen; Song, Rencheng; Cheng, Juan; Liu, Yu; Chen, Xun

EEG-based Emotion Recognition via Neural Architecture Search Journal Article

In: IEEE Transactions on Affective Computing, pp. 1-1, 2021.

1069.

Mazumder, Arnab Neelim; Meng, Jian; Rashid, Hasib-Al; Kallakuri, Utteja; Zhang, Xin; Seo, Jae-Sun; Mohsenin, Tinoosh

A Survey on the Optimization of Neural Network Accelerators for Micro-AI On-Device Inference Journal Article

In: IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 11, no. 4, pp. 532-547, 2021.

1068.

Wang, Ruixin; Jiang, Hongkai; Li, XingQiu; Yao, Pei

A Network Structure Search Method Based on Reinforcement Learning for Rolling Bearing Fault Diagnosis Proceedings Article

In: 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing), pp. 1-5, 2021.

1067.

Huang, Tao; You, Shan; Wang, Fei; Qian, Chen; Zhang, Changshui; Wang, Xiaogang; Xu, Chang

GreedyNASv2: Greedier Search with a Greedy Path Filter Technical Report

2021.

1066.

Sun, Zihao; Hu, Yu; Yang, Longxing; Lu, Shun; Mei, Jilin; Han, Yinhe; Li, Xiaowei

STC-NAS: Fast Neural Architecture Search with Source-Target Consistency Journal Article

In: Neurocomputing, 2021, ISSN: 0925-2312.

1065.

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

Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search Technical Report

2021.

1064.

Arican, Metin Ersin; Kara, Ozgur; Bredell, Gustav; Konukoglu, Ender

ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior Proceedings Article

In: CVPR 2022, 2021.

1063.

Zhang, Miao; Hu, Jilin; Su, Steven; Pan, Shirui; Chang, Xiaojun; Yang, Bin; Haffari, Gholamreza

BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule Technical Report

2021.

1062.

Ying, Guohao; He, Xin; Gao, Bin; Han, Bo; Chu, Xiaowen

EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs Technical Report

2021.

1061.

Abbasi, Saad; Wong, Alexander; Shafiee, Mohammad Javad

MAPLE: Microprocessor A Priori for Latency Estimation Technical Report

2021.

1060.

Qi, Hua; Wang, Zhijie; Guo, Qing; Chen, Jianlang; Juefei-Xu, Felix; Ma, Lei; Zhao, Jianjun

ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks Technical Report

2021.

1059.

Xu, Jin; Chen, Mingjian; Huang, Jianqiang; Tang, Xingyuan; Hu, Ke; Li, Jian; Cheng, Jia; Lei, Jun

AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020 Technical Report

2021.

1058.

Zhou, Yuanding; Li, Baopu; Wang, Zhihui; Li, Haojie

Video Action Recognition with Neural Architecture Search Proceedings Article

In: Balasubramanian, Vineeth N.; Tsang, Ivor W. (Ed.): Asian Conference on Machine Learning, ACML 2021, 17-19 November 2021, Virtual Event, pp. 1675–1690, PMLR, 2021.

1057.

Gala, Jay; Xie, Pengtao

Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search Technical Report

2021.

1056.

Zhu, Yichen; Du, Jie; Zhu, Yuqin; Wang, Yi; Ou, Zhicai; Feng, Feifei; Tang, Jian

Training BatchNorm Only in Neural Architecture Search and Beyond Technical Report

2021.

1055.

Yang, Longxing; Hu, Yu; Lu, Shun; Sun, Zihao; Mei, Jilin; Zeng, Yiming; Shi, Zhiping; Han, Yinhe; Li, Xiaowei

DDSAS: Dynamic and Differentiable Space-Architecture Search Proceedings Article

In: Balasubramanian, Vineeth N.; Tsang, Ivor W. (Ed.): Asian Conference on Machine Learning, ACML 2021, 17-19 November 2021, Virtual Event, pp. 284–299, PMLR, 2021.

1054.

Zhang, Ruisi; Liang, Youwei; Somayajula, Sai Ashish; Xie, Pengtao

Improving Differentiable Architecture Search with a Generative Model Technical Report

2021.

1053.

Liu, Zechun; Shen, Zhiqiang; Long, Yun; Xing, Eric P.; Cheng, Kwang-Ting; Leichner, Chas

Data-Free Neural Architecture Search via Recursive Label Calibration Technical Report

2021.

1052.

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

RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition Technical Report

2021.

1051.

Do, Tu; Luong, Ngoc Hoang

Training-Free Multi-objective Evolutionary Neural Architecture Search via Neural Tangent Kernel and Number of Linear Regions Proceedings Article

In: Mantoro, Teddy; Lee, Minho; Ayu, Media Anugerah; Wong, Kok Wai; Hidayanto, Achmad Nizar (Ed.): Neural Information Processing - 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8-12, 2021, Proceedings, Part II, pp. 335–347, Springer, 2021.

1050.

Liu, Zitu; Zhu, Kai; Liu, Yue; Liu, Qun; Wang, Guoyin; Guo, Yike

Interpretability Analysis Method Based on Path Decomposition for Cell-based Neural Architecture Search Proceedings Article

In: 2021 7th International Conference on Big Data and Information Analytics (BigDIA), pp. 9-16, 2021.

1049.

Cai, Jinhang; Ou, Yimin; Li, Xiu; Wang, Haoqian

ST-NAS: Efficient Optimization of Joint Neural Architecture and Hyperparameter Proceedings Article

In: Mantoro, Teddy; Lee, Minho; Ayu, Media Anugerah; Wong, Kok Wai; Hidayanto, Achmad Nizar (Ed.): Neural Information Processing, pp. 274–281, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-92307-5.

Abstract | BibTeX

1048.

Shi, Jiachen; Fan, Yi; Zhou, Guoqiang; Shen, Jun

Distributed GAN: Towards A Faster Reinforcement Learning Based Architecture Search Journal Article

In: IEEE Transactions on Artificial Intelligence, pp. 1-1, 2021.

1047.

Gong, Saijun; Chen, Lin; Dong, Zhicheng

Neural Network Pruning via Genetic Wavelet Channel Search Proceedings Article

In: Mantoro, Teddy; Lee, Minho; Ayu, Media Anugerah; Wong, Kok Wai; Hidayanto, Achmad Nizar (Ed.): Neural Information Processing, pp. 348–358, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-92270-2.

@inproceedings{10.1007/978-3-030-92270-2_30,

title = {Neural Network Pruning via Genetic Wavelet Channel Search},

author = {Saijun Gong and Lin Chen and Zhicheng Dong},

editor = {Teddy Mantoro and Minho Lee and Media Anugerah Ayu and Kok Wai Wong and Achmad Nizar Hidayanto},

url = {https://link.springer.com/chapter/10.1007/978-3-030-92270-2_30},

isbn = {978-3-030-92270-2},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Neural Information Processing},

pages = {348--358},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Neural network pruning has been commonly adopted for alleviating the computational cost of resource-limited devices. Neural architecture search (NAS) is an efficient approach to facilitate neural network compression. However, most existing NAS-based algorithms focus on searching the best sub-network architecture at the layer level, while ignoring the channels that contain richer information. Meanwhile, fixing one pruning rate could not be suitable for all the layers with various features. In this paper, we present a novel NAS-based network pruning strategy called genetic wavelet channel search (GWCS) model, which automatically prunes the network at the channel level with two-fold ideas: (a) Each channel in the whole network is gene coded and pruned adaptively in multiple stages with dynamic genetic selection, and (b) the fitness function of channels is carefully designed by calculating the similarity between the pre-trained network and pruned network using wavelet decomposition to discover the most representative and discriminative channels. Extensive experiments are conducted on CIFAR-10 and CIFAR-100 using ResNet series networks. The proposed GWCS method outperforms other existing pruning approaches in terms of accuracy with a higher compression rate. Our source code is freely available at https://github.com/MGongsj/Network-pruning.git.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

1046.

Tian, Yunjie; Xie, Lingxi; Fang, Jiemin; Jiao, Jianbin; Ye, Qixiang; Tian, Qi

Exploring Complicated Search Spaces with Interleaving-Free Sampling Technical Report

2021.

1045.

Liu, Jinfeng; Kong, Lingtong; Yang, Jie

Designing and Searching for Lightweight Monocular Depth Network Proceedings Article

In: Mantoro, Teddy; Lee, Minho; Ayu, Media Anugerah; Wong, Kok Wai; Hidayanto, Achmad Nizar (Ed.): Neural Information Processing, pp. 477–488, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-92273-3.

@inproceedings{10.1007/978-3-030-92273-3_39,

title = {Designing and Searching for Lightweight Monocular Depth Network},

author = {Jinfeng Liu and Lingtong Kong and Jie Yang},

editor = {Teddy Mantoro and Minho Lee and Media Anugerah Ayu and Kok Wai Wong and Achmad Nizar Hidayanto},

url = {https://link.springer.com/chapter/10.1007/978-3-030-92273-3_39},

isbn = {978-3-030-92273-3},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Neural Information Processing},

pages = {477--488},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Depth sensing is extremely notable in some tasks of robot and autonomous driving. Nowadays, monocular depth estimation based on deep learning becomes a research focus in computer vision. However, most of the current work is seeking for more complex models to get higher accuracy, which can not achieve real-time inference on mobile or embedded systems. Therefore, we aim to design a lightweight model in this paper. At first, we improve the state-of-the-art model, FastDepth, producing FastDepthV2, which has higher accuracy and lower latency on the NYU Depth v2 dataset. Besides, since designing artificial networks takes time and effort, we make it automatic to design lightweight models in monocular depth estimation, using neural architecture search (NAS). Further, inspired by the architecture of MobileNetV2, a factorized hierarchical search space for encoder is used in this paper. In the meanwhile, we incorporate the accuracy and multiply-add operations of a model together into the searching objective, and use gradient-based reinforcement learning algorithm in the searching iterations. The controller in the reinforcement learning framework has converged after more than 1000 searching iterations, and three network architectures with the best performance are obtained. Under the same conditions of training and testing, two of them perform better than FastDepthV2.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

1044.

Zhou, Yuguang; He, Zheng; Wan, Tao; Qin, Zengchang

Random Neural Graph Generation with Structure Evolution Proceedings Article

In: Mantoro, Teddy; Lee, Minho; Ayu, Media Anugerah; Wong, Kok Wai; Hidayanto, Achmad Nizar (Ed.): Neural Information Processing, pp. 87–98, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-92270-2.

1043.

Laube, Kevin Alexander

The UniNAS framework: combining modules in arbitrarily complex configurations with argument trees Technical Report

2021.

1042.

Nguyen, Giang; Islam, Johir; Pan, Rangeet; Rajan, Hridesh

Manas: Mining Software Repositories to Assist AutoML Technical Report

2021.

1041.

Jiang, Yifan; Gong, Xinyu; Wu, Junru; Shi, Humphrey; Yan, Zhicheng; Wang, Zhangyang

Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search Technical Report

2021.

1040.

Lu, Shun; Hu, Yu; Yang, Longxing; Sun, Zihao; Mei, Jilin; Yiming, Zeng; Li, Xiaowei

DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture Search Proceedings Article

In: BMVC, 2021.

1039.

Bai, Jinyu; Fan, Yunqian; Sun, Sifan; Kang, Wang; Zhao, Weisheng

Tiny neural network search and implementation for embedded FPGA: a software-hardware co-design approach Proceedings Article

In: 2021 IEEE Asian Solid-State Circuits Conference (A-SSCC), pp. 1-3, 2021.

1038.

Hou, Boyu; Dong, Junwei; Feng, Liang; Qiu, Minghui

Efficient Two-Stage Evolutionary Search of Convolutional Neural Architectures Based on Cell Independence Analysis Proceedings Article

In: Mantoro, Teddy; Lee, Minho; Ayu, Media Anugerah; Wong, Kok Wai; Hidayanto, Achmad Nizar (Ed.): Neural Information Processing, pp. 599–607, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-92307-5.

1037.

Liu, Risheng; Ma, Long; Ma, Tengyu; Fan, Xin; Luo, Zhongxuan

Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision Technical Report

2021.

1036.

Wang, He; Zhu, Xinshan; Chen, Pinyin; Yang, Yuxuan; Ma, Chao; Gao, Zhongke

A gradient-based automatic optimization CNN framework for EEG state recognition Journal Article

In: Journal of Neural Engineering, 2021.

@article{10.1088/1741-2552/ac41ac,

title = {A gradient-based automatic optimization CNN framework for EEG state recognition},

author = {He Wang and Xinshan Zhu and Pinyin Chen and Yuxuan Yang and Chao Ma and Zhongke Gao},

url = {http://iopscience.iop.org/article/10.1088/1741-2552/ac41ac},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Journal of Neural Engineering},

abstract = {The Electroencephalogram (EEG) signal, as a data carrier that can contain a large amount of information about the human brain in different states, is one of the most widely used metrics for assessing human psychophysiological states. Among a variety of analysis methods, deep learning, especially convolutional neural network (CNN), has achieved remarkable results in recent years as a method to effectively extract features from EEG signals. Although deep learning has the advantages of automatic feature extraction and effective classification, it also faces difficulties in network structure design and requires an army of prior knowledge. Automating the design of these hyperparameters can therefore save experts' time and manpower. Neural architecture search techniques have thus emerged. In this paper, based on an existing gradient-based NAS algorithm, PC-DARTS, with targeted improvements and optimizations for the characteristics of EEG signals. Specifically, we establish the model architecture step by step based on the manually designed deep learning models for EEG discrimination by retaining the framework of the search algorithm and performing targeted optimization of the model search space. Corresponding features are extracted separately according to the frequency domain, time domain characteristics of the EEG signal and the spatial position of the EEG electrode. The architecture was applied to EEG-based emotion recognition and driver drowsiness assessment tasks. The results illustrate that compared with the existing methods, the model architecture obtained in this paper can achieve competitive overall accuracy and better standard deviation in both tasks. Therefore, this approach is an effective migration of NAS technology into the field of EEG analysis and has great potential to provide high-performance results for other types of classification and prediction tasks. This can effectively reduce the time cost for researchers and facilitate the application of CNN in more areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

The Electroencephalogram (EEG) signal, as a data carrier that can contain a large amount of information about the human brain in different states, is one of the most widely used metrics for assessing human psychophysiological states. Among a variety of analysis methods, deep learning, especially convolutional neural network (CNN), has achieved remarkable results in recent years as a method to effectively extract features from EEG signals. Although deep learning has the advantages of automatic feature extraction and effective classification, it also faces difficulties in network structure design and requires an army of prior knowledge. Automating the design of these hyperparameters can therefore save experts' time and manpower. Neural architecture search techniques have thus emerged. In this paper, based on an existing gradient-based NAS algorithm, PC-DARTS, with targeted improvements and optimizations for the characteristics of EEG signals. Specifically, we establish the model architecture step by step based on the manually designed deep learning models for EEG discrimination by retaining the framework of the search algorithm and performing targeted optimization of the model search space. Corresponding features are extracted separately according to the frequency domain, time domain characteristics of the EEG signal and the spatial position of the EEG electrode. The architecture was applied to EEG-based emotion recognition and driver drowsiness assessment tasks. The results illustrate that compared with the existing methods, the model architecture obtained in this paper can achieve competitive overall accuracy and better standard deviation in both tasks. Therefore, this approach is an effective migration of NAS technology into the field of EEG analysis and has great potential to provide high-performance results for other types of classification and prediction tasks. This can effectively reduce the time cost for researchers and facilitate the application of CNN in more areas.

1035.

Lee, Jaeseong; Rhim, Jungsub; Kang, Duseok; Ha, Soonhoi

S3NAS: Fast Hardware-aware Neural Architecture Search Methodology Journal Article

In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-1, 2021.

1034.

Zhou, Yao; Yuan, Xianglei; Zhang, Xiaozhi; Liu, Wei; Wu, Yu; Yen, Gary G.; Hu, Bing; Zhang, Yi

Evolutionary Neural Architecture Search for Automatic Esophageal Lesion Identification and Segmentation Journal Article

In: IEEE Transactions on Artificial Intelligence, pp. 1-1, 2021.

1033.

Chen, Donghui; Chen, Ling; Shang, Zongjiang; Zhang, Youdong; Wen, Bo; Yang, Chenghu

Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting Technical Report

2021.

1032.

Li, Xiaobin; Jiang, Hongxu; Tian, Fangzheng; Zhang, Yonghua; Miao, Rui; Xu, Donghuan

Optimal FPGA-oriented lightweight network architecture search under multi-objective constraints Proceedings Article

In: 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), New York City, NY, USA, September 30 - Oct. 3, 2021, pp. 978–983, IEEE, 2021.

1031.

Huang, Zhenhan; Jiang, Chunheng; Chen, Pin-Yu; Gao, Jianxi

Network Graph Based Neural Architecture Search Technical Report

2021.