AutoML | Literature on Neural Architecture Search

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.

Abstract | Links | BibTeX

@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}

}

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1046.

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

Exploring Complicated Search Spaces with Interleaving-Free Sampling Technical Report

2021.

Links | BibTeX

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.

Abstract | Links | BibTeX

@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}

}

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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.

Abstract | Links | BibTeX

1043.

Laube, Kevin Alexander

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

2021.

Links | BibTeX

1042.

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

Manas: Mining Software Repositories to Assist AutoML Technical Report

2021.

Links | BibTeX

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.

Links | BibTeX

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.

Links | BibTeX

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.

Links | BibTeX

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.

Abstract | Links | BibTeX

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.

Links | BibTeX

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.

Abstract | Links | BibTeX

@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}

}

Close

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.

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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.

Links | BibTeX

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.

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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.

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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.

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1031.

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

Network Graph Based Neural Architecture Search Technical Report

2021.

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1030.

Carmichael, Zachariah; Moon, Tim; Jacobs, Sam Ade

Learning Interpretable Models Through Multi-Objective Neural Architecture Search Technical Report

2021.

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1029.

Kuang, Huiyu

M-FasterSeg: An Efficient Semantic Segmentation Network Based on Neural Architecture Search Technical Report

2021.

Links | BibTeX

1028.

Kung, Chi-Hsi; Lee, Che-Rung

ADD: A Fine-grained Dynamic Inference Architecture for Semantic Image Segmentation Proceedings Article

In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4792-4799, 2021.

Links | BibTeX

1027.

Peng, Cheng; Myronenko, Andriy; Hatamizadeh, Ali; Nath, Vishwesh; Siddiquee, Md Mahfuzur Rahman; He, Yufan; Xu, Daguang; Chellappa, Rama; Yang, Dong

HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet Technical Report

2021.

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1026.

Dong, Xuanyi; Kedziora, David Jacob; Musial, Katarzyna; Gabrys, Bogdan

Automated Deep Learning: Neural Architecture Search Is Not the End Technical Report

2021.

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1025.

Shi, Xian; Zhou, Pan; Chen, Wei; Xie, Lei

Efficient Gradient-Based Neural Architecture Search For End-to-End ASR Proceedings Article

In: Companion Publication of the 2021 International Conference on Multimodal Interaction, pp. 91–96, Association for Computing Machinery, Montreal, QC, Canada, 2021, ISBN: 9781450384711.

Abstract | Links | BibTeX

1024.

Muñoz, J. Pablo; Lyalyushkin, Nikolay; Akhauri, Yash; Senina, Anastasia; Kozlov, Alexander; Jain, Nilesh

Enabling NAS with Automated Super-Network Generation Technical Report

2021.

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1023.

Wu, Xinle; Zhang, Dalin; Guo, Chenjuan; He, Chaoyang; Yang, Bin; Jensen, Christian S.

AutoCTS: Automated Correlated Time Series Forecasting - Extended Version Technical Report

2021.

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1022.

Khan, Tariq Mahmood; Naqvi, Syed Saud; Meijering, Erik

Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation Technical Report

2021.

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1021.

Zhang, Zhida; Huang, Peng; Mao, Xiangyu; Sun, Yongwen; Liu, Yang; Shao, Huiqin

Surface defect detection method based on neural architecture search Proceedings Article

In: 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC), pp. 612-617, 2021.

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1020.

Luo, Ziyuan; Nguyen, Minh; Yan, Wei Qi

Sailboat Detection Based on Automated Search Attention Mechanism and Deep Learning Models Proceedings Article

In: 2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1-6, 2021.

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1019.

Wu, Robert (Rupert); Jain, Rohan

QENAS: Q-Learning for Efficient Neural Architecture Search Technical Report

2021.

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1018.

Wei, Lanning; Zhao, Huan; He, Zhiqiang

Learn Layer-wise Connections in Graph Neural Networks Technical Report

2021.

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1017.

Wang, I Sheng; Chan, Hung-Tse; Hsia, Chih-Hsien

Finger-Vein Recognition Using a NASNet with a Cutout Proceedings Article

In: 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 1-2, 2021.

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1016.

Fil, Miroslav; Ru, Binxin; Lyle, Clare; Gal, Yarin

DARTS without a Validation Set: Optimizing the Marginal Likelihood Technical Report

2021.

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1015.

Xia, Xin; Xiao, Xuefeng; Wang, Xing; Zheng, Min

Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search Proceedings Article

In: WACV2022, 2021.

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1014.

Mushtaq, Erum; He, Chaoyang; Ding, Jie; Avestimehr, Salman

SPIDER: Searching Personalized Neural Architecture for Federated Learning Technical Report

2021.

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1013.

Wei, Lanning; Zhao, Huan; He, Zhiqiang

Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective Technical Report

2021.

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1012.

Wang, Zhaolei; Zhang, Jun; Li, Yue; Gong, Qinghai; Luo, Wuyi; Zhao, Jikang

Automated Reinforcement Learning Based on Parameter Sharing Network Architecture Search Proceedings Article

In: 2021 6th International Conference on Robotics and Automation Engineering (ICRAE), pp. 358-363, 2021.

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1011.

Xu, Shuying; Quan, Hongyan

ECT-NAS: Searching Efficient CNN-Transformers Architecture for Medical Image Segmentation Proceedings Article

In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1601-1604, 2021.

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1010.

Kulbach, Cedric; Thoma, Steffen

Personalized Neural Architecture Search Proceedings Article

In: 2021 International Conference on Data Mining Workshops (ICDMW), pp. 581-590, 2021.

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1009.

Termritthikun, Chakkrit; Xu, Lin; Liu, Yemeng; Lee, Ivan

Neural Architecture Search and Multi-Objective Evolutionary Algorithms for Anomaly Detection Proceedings Article

In: 2021 International Conference on Data Mining Workshops (ICDMW), pp. 1001-1008, 2021.

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1008.

Fu, Jun; Hou, Chen; Chen, Zhibo

AutoDerain: Memory-efficient Neural Architecture Search for Image Deraining Proceedings Article

In: 2021 International Conference on Visual Communications and Image Processing (VCIP), pp. 1-5, 2021.

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1007.

Xuanhui, Dai; Juan, Chen; Quan, Wen

Network Pruning Based On Architecture Search and Intermediate Representation Proceedings Article

In: 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 230-233, 2021.

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1006.

Zhang, Jiuling; Ding, Zhiming

Robustifying DARTS by Eliminating Information Bypass Leakage via Explicit Sparse Regularization Proceedings Article

In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 877-885, 2021.

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1005.

Ravishankar, Anand; Natarajan, Santhi; Malakreddy, A Bharathi

Pruned Genetic-NAS on GPU Accelerator Platforms with Chaos-on-Edge Hyperparameters Proceedings Article

In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 958-963, 2021.

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1004.

Hasan, Noha W.; Saudi, Ali S.; Khalil, Mahmoud I.; Abbas, Hazem M.

E-DARTS: Enhanced Differentiable Architecture Search for Acoustic Scene Classification Proceedings Article

In: 2021 16th International Conference on Computer Engineering and Systems (ICCES), pp. 1-6, 2021.

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1003.

Zhang, Feifei; Ge, Jidong; Wong, Chifong; Zhang, Sheng; Li, Chuanyi; Luo, Bin

Optimizing Federated Edge Learning on Non-IID Data via Neural Architecture Search Proceedings Article

In: 2021 IEEE Global Communications Conference (GLOBECOM), pp. 1-6, 2021.

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1002.

Salam, Hanan; Celiktutan, Oya; Mukherjee, Himadri; Manoranjan, Viswonathan; Ismail, Iman

Fact sheet: Automatic self-reported personality recognition Track Proceedings Article

In: ICCV 2021 Understanding Social Behavior in Dyadic and Small Group Interactions Challenge, 2021.

Abstract | Links | BibTeX

@inproceedings{8bd8f43c88834128b77c4707d609d8b8,

title = {Fact sheet: Automatic self-reported personality recognition Track},

author = {Hanan Salam and Oya Celiktutan and Himadri Mukherjee and Viswonathan Manoranjan and Iman Ismail},

url = {https://nyuscholars.nyu.edu/en/publications/fact-sheet-automatic-self-reported-personality-recognition-track},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {ICCV 2021 Understanding Social Behavior in Dyadic and Small Group Interactions Challenge},

abstract = {Smart phones, voice assistants, and home robots are becoming more intelligent every day to support humans in their daily routines and tasks. Achieving the user acceptance and success of such technologies makes it necessary for them to be socially informed, responsive, and responsible. They need to understand human behaviour and socio-emotional states and adapt themselves to their user’s profiles (eg, personality) and preferences. Motivated by this, there has been a significant effort in recognising personality from multimodal data in the last decade [1],[2]. However, to the best of our knowledge, the methods so far have focused on one-fits-all approaches only and performed personality recognition without taking into consideration the user’s profiles (eg, gender and age). In this paper, we took a different approach, and we argued that one-fits-all approach does not work sufficiently for personality recognition as previous research showed that there are significant gender differences in personality traits. For example, women tend to report higher scores for extraversion, agreeableness and neuroticsm as compared to men [3]. Building upon these findings, we first clustered the participants into two profiles based on their gender, namely, female and male, and then used Neural Architecture Search (NAS) to automatically design a model for each profile to recognise personality. A separate network was designed and trained with visual and text features. The final prediction was obtained by aggregating the results of both video and text modalities. Figure 1 presents the overview of our proposed approach.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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1001.

Phan, Quan Minh; Luong, Ngoc Hoang

Efficiency Enhancement of Evolutionary Neural Architecture Search via Training-Free Initialization Proceedings Article

In: 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), pp. 138-143, 2021.

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