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.
2024
Luo, Zhirui
DEEP LEARNING METHODS FOR SMART METER DATA ANALYTICS AND APPLICATIONS PhD Thesis
2024.
@phdthesis{luo-phd23a,
title = {DEEP LEARNING METHODS FOR SMART METER DATA ANALYTICS AND APPLICATIONS},
author = {Zhirui Luo},
url = {https://www.proquest.com/docview/2915480009?pq-origsite=gscholar&fromopenview=true&sourcetype=Dissertations%20&%20Theses},
year = {2024},
date = {2024-03-01},
urldate = {2024-03-01},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
(Ed.)
PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search Collection
2024.
@collection{Yao-aaai24a,
title = {PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search},
author = {Dixi Yao and Baochun Li},
url = {https://iqua.ece.toronto.edu/papers/dixiyao-aaai24.pdf},
year = {2024},
date = {2024-03-01},
urldate = {2024-03-01},
booktitle = {AAAI 2024},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
Yang, Yi; Wei, Jiaxuan; Yu, Yhixuan; Zhang, Ruisheng
Multi-label neural architecture search for chest radiography image classification Journal Article
In: Multimedia Systems, 2024.
@article{nokey,
title = {Multi-label neural architecture search for chest radiography image classification},
author = {Yi Yang and Jiaxuan Wei and Yhixuan Yu and Ruisheng Zhang},
url = {https://link.springer.com/article/10.1007/s00530-023-01215-6},
year = {2024},
date = {2024-01-13},
urldate = {2024-01-13},
journal = { Multimedia Systems},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
LI, PEIXIA
Deep Neural Networks for Visual Object Tracking: An Investigation of Performance Optimization PhD Thesis
2024.
@phdthesis{li-phd23a,
title = {Deep Neural Networks for Visual Object Tracking: An Investigation of Performance Optimization},
author = {PEIXIA LI},
url = {https://scholar.google.de/scholar_url?url=https://ses.library.usyd.edu.au/bitstream/handle/2123/32052/li_p_thesis.pdf%3Fsequence%3D1%26isAllowed%3Dy&hl=de&sa=X&d=18223269356716447880&ei=EsGfZemVKsKAy9YP4bqs-Ac&scisig=AFWwaeYD2Xl7kLWLITGKadrDAlTa&oi=scholaralrt&hist=mvciDDAAAAAJ:2945779489622371749:AFWwaeYSwjSBxI9k5p1JRsFqGwve&html=&pos=2&folt=kw},
year = {2024},
date = {2024-01-12},
urldate = {2024-01-12},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Dong, Minjing
Boosting Adversarial Robustness via Neural Architecture Search and Design PhD Thesis
2024.
@phdthesis{dong-phd23a,
title = {Boosting Adversarial Robustness via Neural Architecture Search and Design},
author = {Minjing Dong },
url = {https://ses.library.usyd.edu.au/bitstream/handle/2123/32060/dong_md_thesis.pdf?sequence=1&isAllowed=y},
year = {2024},
date = {2024-01-12},
urldate = {2024-01-12},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
(Ed.)
ENHANCING GAN PERFORMANCE THROUGH NEURAL ARCHITECTURE SEARCH AND TENSOR DECOMPOSITION Collection
2024.
@collection{Pulakurthi-icassp24a,
title = {ENHANCING GAN PERFORMANCE THROUGH NEURAL ARCHITECTURE SEARCH AND TENSOR DECOMPOSITION},
author = {Prasanna Reddy Pulakurthi and Mahsa Mozaffari and Majid Rabbani and Jamison Heard and Raghuveer Rao},
url = {https://mahsamozaffari.com/wp-content/uploads/2023/11/ICASSP_2024.pdf},
year = {2024},
date = {2024-01-02},
urldate = {2024-01-02},
booktitle = {ICASSP 2024},
keywords = {},
pubstate = {published},
tppubtype = {collection}
}
Wu, Fan; Gao, Jinling; Hong, Lanqing; Wang, Xinbing; Zhou, Chenghu; Ye, Nanyang
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-04672,
title = {G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection},
author = {Fan Wu and Jinling Gao and Lanqing Hong and Xinbing Wang and Chenghu Zhou and Nanyang Ye},
url = {https://doi.org/10.48550/arXiv.2402.04672},
doi = {10.48550/ARXIV.2402.04672},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.04672},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Zeng, Junhua; Zhou, Guoxu; Li, Chao; Sun, Zhun; Zhao, Qibin
Discovering More Effective Tensor Network Structure Search Algorithms via Large Language Models (LLMs) Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-02456,
title = {Discovering More Effective Tensor Network Structure Search Algorithms via Large Language Models (LLMs)},
author = {Junhua Zeng and Guoxu Zhou and Chao Li and Zhun Sun and Qibin Zhao},
url = {https://doi.org/10.48550/arXiv.2402.02456},
doi = {10.48550/ARXIV.2402.02456},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.02456},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Dong, Peijie; Li, Lujun; Pan, Xinglin; Wei, Zimian; Liu, Xiang; Wang, Qiang; Chu, Xiaowen
ParZC: Parametric Zero-Cost Proxies for Efficient NAS Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-02105,
title = {ParZC: Parametric Zero-Cost Proxies for Efficient NAS},
author = {Peijie Dong and Lujun Li and Xinglin Pan and Zimian Wei and Xiang Liu and Qiang Wang and Xiaowen Chu},
url = {https://doi.org/10.48550/arXiv.2402.02105},
doi = {10.48550/ARXIV.2402.02105},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.02105},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Risso, Matteo; Xie, Chen; Daghero, Francesco; Burrello, Alessio; Mollaei, Seyedmorteza; Castellano, Marco; Macii, Enrico; Poncino, Massimo; Pagliari, Daniele Jahier
HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-01226,
title = {HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays},
author = {Matteo Risso and Chen Xie and Francesco Daghero and Alessio Burrello and Seyedmorteza Mollaei and Marco Castellano and Enrico Macii and Massimo Poncino and Daniele Jahier Pagliari},
url = {https://doi.org/10.48550/arXiv.2402.01226},
doi = {10.48550/ARXIV.2402.01226},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.01226},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Zouambi, Meyssa
Optimizing Deep Learning: Navigating the Field of Neural Architecture Search from Theory to Practice PhD Thesis
2024.
@phdthesis{Zouambi-phd24a,
title = {Optimizing Deep Learning: Navigating the Field of Neural Architecture Search from Theory to Practice},
author = {Meyssa Zouambi},
url = {https://hal.science/tel-04437745/document},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Zhang, Baochang; Wang, Tiancheng; Xu, Sheng; Doermann, David
Binary Neural Architecture Search Book Chapter
In: Neural Networks with Model Compression, pp. 49–99, Springer Nature Singapore, Singapore, 2024, ISBN: 978-981-99-5068-3.
@inbook{Zhang2024,
title = {Binary Neural Architecture Search},
author = {Baochang Zhang and Tiancheng Wang and Sheng Xu and David Doermann},
url = {https://doi.org/10.1007/978-981-99-5068-3_3},
doi = {10.1007/978-981-99-5068-3_3},
isbn = {978-981-99-5068-3},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Neural Networks with Model Compression},
pages = {49–99},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Deep convolutional neural networks (DCNNs) have achieved state-of-the-art performance in various computer vision tasks, including image classification, instance segmentation, and object detection. The success of DCNNs is attributed to effective architecture design. Neural architecture search (NAS) is an emerging approach that automates the process of designing neural architectures, replacing manual design.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Tempel, Felix; Strümke, Inga; Ihlen, Espen Alexander Fürst
AutoGCN - Towards Generic Human Activity Recognition with Neural Architecture Search Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-01313,
title = {AutoGCN - Towards Generic Human Activity Recognition with Neural Architecture Search},
author = {Felix Tempel and Inga Strümke and Espen Alexander Fürst Ihlen},
url = {https://doi.org/10.48550/arXiv.2402.01313},
doi = {10.48550/ARXIV.2402.01313},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.01313},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Zhang, Sheng; Wang, Maolin; Zhao, Yao; Zhuang, Chenyi; Gu, Jinjie; Guo, Ruocheng; Zhao, Xiangyu; Zhang, Zijian; Yin, Hongzhi
EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2402-00390,
title = {EASRec: Elastic Architecture Search for Efficient Long-term Sequential Recommender Systems},
author = {Sheng Zhang and Maolin Wang and Yao Zhao and Chenyi Zhuang and Jinjie Gu and Ruocheng Guo and Xiangyu Zhao and Zijian Zhang and Hongzhi Yin},
url = {https://doi.org/10.48550/arXiv.2402.00390},
doi = {10.48550/ARXIV.2402.00390},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2402.00390},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Pižurica, Nikola; Pavlović, Kosta; Kovačević, Slavko; Jovančević, Igor; Prado, Miguel
In: Journal of Electronic Imaging, vol. 33, no. 3, pp. 031203, 2024.
@article{10.1117/1.JEI.33.3.031203,
title = {Generic neural architecture search toolkit for efficient and real-world deployment of visual inspection convolutional neural networks in industry},
author = {Nikola Pižurica and Kosta Pavlović and Slavko Kovačević and Igor Jovančević and Miguel Prado},
url = {https://doi.org/10.1117/1.JEI.33.3.031203},
doi = {10.1117/1.JEI.33.3.031203},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Journal of Electronic Imaging},
volume = {33},
number = {3},
pages = {031203},
publisher = {SPIE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sheng, Chunyin; Gao, Xiang; Hu, Xiaopeng; Wang, Fan
Differentiable Neural Architecture Search Based on Efficient Architecture for Lightweight Image Super-Resolution Proceedings Article
In: Rudinac, Stevan; Hanjalic, Alan; Liem, Cynthia; Worring, Marcel; Jónsson, Björn Þór; Liu, Bei; Yamakata, Yoko (Ed.): MultiMedia Modeling, pp. 169–183, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-53311-2.
@inproceedings{10.1007/978-3-031-53311-2_13b,
title = {Differentiable Neural Architecture Search Based on Efficient Architecture for Lightweight Image Super-Resolution},
author = {Chunyin Sheng and Xiang Gao and Xiaopeng Hu and Fan Wang},
editor = {Stevan Rudinac and Alan Hanjalic and Cynthia Liem and Marcel Worring and Björn Þór Jónsson and Bei Liu and Yoko Yamakata},
url = {https://link.springer.com/chapter/10.1007/978-3-031-53311-2_13},
isbn = {978-3-031-53311-2},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {MultiMedia Modeling},
pages = {169–183},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {With the advancement of deep neural networks, image Super-Resolution (SR) has witnessed remarkable improvements in performance. However, the increasing number of parameters and computational complexity has posed challenges for the practical deployment of SR models. To address these challenges, we propose a novel approach called Differentiable Neural Architecture Search (NAS) based on Efficient Architecture for lightweight image Super-Resolution, referred to as DNAS-EASR. In DNAS-EASR, we employ the information distillation mechanism (IDM) at the cell-level space to search for key operations. Additionally, we search for attention modules at the cell-level space to determine the most suitable attention module for our architecture. Furthermore, we adopt a hierarchical architecture as our backbone network to enable multi-scale information processing and fusion. Extensive experiments conducted on benchmark datasets demonstrate that DNAS-EASR is lightweight, efficient and capable of achieving comparable performance to other lightweight methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Sheng, Chunyin; Gao, Xiang; Hu, Xiaopeng; Wang, Fan
Differentiable Neural Architecture Search Based on Efficient Architecture for Lightweight Image Super-Resolution Proceedings Article
In: Rudinac, Stevan; Hanjalic, Alan; Liem, Cynthia; Worring, Marcel; Jónsson, Björn Þór; Liu, Bei; Yamakata, Yoko (Ed.): MultiMedia Modeling, pp. 169–183, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-53311-2.
@inproceedings{10.1007/978-3-031-53311-2_13,
title = {Differentiable Neural Architecture Search Based on Efficient Architecture for Lightweight Image Super-Resolution},
author = {Chunyin Sheng and Xiang Gao and Xiaopeng Hu and Fan Wang},
editor = {Stevan Rudinac and Alan Hanjalic and Cynthia Liem and Marcel Worring and Björn Þór Jónsson and Bei Liu and Yoko Yamakata},
url = {https://link.springer.com/chapter/10.1007/978-3-031-53311-2_13},
isbn = {978-3-031-53311-2},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {MultiMedia Modeling},
pages = {169–183},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {With the advancement of deep neural networks, image Super-Resolution (SR) has witnessed remarkable improvements in performance. However, the increasing number of parameters and computational complexity has posed challenges for the practical deployment of SR models. To address these challenges, we propose a novel approach called Differentiable Neural Architecture Search (NAS) based on Efficient Architecture for lightweight image Super-Resolution, referred to as DNAS-EASR. In DNAS-EASR, we employ the information distillation mechanism (IDM) at the cell-level space to search for key operations. Additionally, we search for attention modules at the cell-level space to determine the most suitable attention module for our architecture. Furthermore, we adopt a hierarchical architecture as our backbone network to enable multi-scale information processing and fusion. Extensive experiments conducted on benchmark datasets demonstrate that DNAS-EASR is lightweight, efficient and capable of achieving comparable performance to other lightweight methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Wang, Shuaishuai; Sun, Yixiang; Sha, Yetong; Yang, Guangyu; Cheng, Dongzhou; Zhang, Lei; Wu, Hao; Song, Aiguo
Robust Human Activity Recognition via Wearable Sensors Using Dynamic Gaussian Kernel Learning Journal Article
In: IEEE Sensors Journal, pp. 1-1, 2024.
@article{10413952,
title = {Robust Human Activity Recognition via Wearable Sensors Using Dynamic Gaussian Kernel Learning},
author = {Shuaishuai Wang and Yixiang Sun and Yetong Sha and Guangyu Yang and Dongzhou Cheng and Lei Zhang and Hao Wu and Aiguo Song},
url = {https://ieeexplore.ieee.org/abstract/document/10413952},
doi = {10.1109/JSEN.2024.3355704},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Sensors Journal},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gao, Zhaoqi; Wang, Kezheng; Wang, Zhiguo; Gao, Jinghuai
Optimizing Seismic Facies Classification Through Differentiable Network Architecture Search Journal Article
In: IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-12, 2024.
@article{10413525,
title = {Optimizing Seismic Facies Classification Through Differentiable Network Architecture Search},
author = {Zhaoqi Gao and Kezheng Wang and Zhiguo Wang and Jinghuai Gao},
url = {https://ieeexplore.ieee.org/abstract/document/10413525},
doi = {10.1109/TGRS.2024.3357929},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {62},
pages = {1-12},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lyu, Bo; Yang, Yin; Cao, Yuting; Wang, Pengcheng; Zhu, Jian; Chang, Jingfei; Wen, Shiping
Efficient multi-objective neural architecture search framework via policy gradient algorithm Journal Article
In: Information Sciences, vol. 661, pp. 120186, 2024, ISSN: 0020-0255.
@article{LYU2024120186,
title = {Efficient multi-objective neural architecture search framework via policy gradient algorithm},
author = {Bo Lyu and Yin Yang and Yuting Cao and Pengcheng Wang and Jian Zhu and Jingfei Chang and Shiping Wen},
url = {https://www.sciencedirect.com/science/article/pii/S0020025524000999},
doi = {https://doi.org/10.1016/j.ins.2024.120186},
issn = {0020-0255},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Information Sciences},
volume = {661},
pages = {120186},
abstract = {Differentiable architecture search plays a prominent role in Neural Architecture Search (NAS) and exhibits preferable efficiency than traditional heuristic NAS methods, including those based on evolutionary algorithms (EA) and reinforcement learning (RL). However, differentiable NAS methods encounter challenges when dealing with non-differentiable objectives like energy efficiency, resource constraints, and other non-differentiable metrics, especially under multi-objective search scenarios. While the multi-objective NAS research addresses these challenges, the individual training required for each candidate architecture demands significant computational resources. To bridge this gap, this work combines the efficiency of the differentiable NAS with metrics compatibility in multi-objective NAS. The architectures are discretely sampled by the architecture parameter α within the differentiable NAS framework, and α are directly optimised by the policy gradient algorithm. This approach eliminates the need for a sampling controller to be learned and enables the encompassment of non-differentiable metrics. We provide an efficient NAS framework that can be readily customized to address real-world multi-objective NAS (MNAS) scenarios, encompassing factors such as resource limitations and platform specialization. Notably, compared with other multi-objective NAS methods, our NAS framework effectively decreases the computational burden (accounting for just 1/6 of the NSGA-Net). This search framework is also compatible with the other efficiency and performance improvement strategies under the differentiable NAS framework.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lu, Yao; Rodriguez, Hiram Rayo Torres; Vogel, Sebastian; Waterlaat, Nick; Jancura, Pavol
Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2401-12350,
title = {Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge},
author = {Yao Lu and Hiram Rayo Torres Rodriguez and Sebastian Vogel and Nick Waterlaat and Pavol Jancura},
url = {https://doi.org/10.48550/arXiv.2401.12350},
doi = {10.48550/ARXIV.2401.12350},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2401.12350},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Gambella, Matteo; Pomponi, Jary; Scardapane, Simone; Roveri, Manuel
NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural Networks Technical Report
2024.
@techreport{gambella2024nachos,
title = {NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural Networks},
author = {Matteo Gambella and Jary Pomponi and Simone Scardapane and Manuel Roveri},
url = {https://arxiv.org/abs/2401.13330},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Cui, Suhan; Wang, Jiaqi; Zhong, Yuan; Liu, Han; Wang, Ting; Ma, Fenglong
Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions Technical Report
2024.
@techreport{cui2024automated,
title = {Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions},
author = {Suhan Cui and Jiaqi Wang and Yuan Zhong and Han Liu and Ting Wang and Fenglong Ma},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Sun, Yize; Wu, Zixin; Ma, Yunpu; Tresp, Volker
Quantum Architecture Search with Unsupervised Representation Learning Technical Report
2024.
@techreport{sun2024quantum,
title = {Quantum Architecture Search with Unsupervised Representation Learning},
author = {Yize Sun and Zixin Wu and Yunpu Ma and Volker Tresp},
url = {https://arxiv.org/abs/2401.11576},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Xue, Yu; Zhang, Zhenman; Neri, Ferrante
Similarity surrogate-assisted evolutionary neural architecture search with dual encoding strategy Technical Report
no. 2, 2024, ISSN: 2688-1594.
@techreport{nokey,
title = {Similarity surrogate-assisted evolutionary neural architecture search with dual encoding strategy},
author = {Yu Xue and Zhenman Zhang and Ferrante Neri},
url = {https://www.aimspress.com/article/doi/10.3934/era.2024050},
doi = {10.3934/era.2024050},
issn = {2688-1594},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Electronic Research Archive},
volume = {32},
number = {2},
pages = {1017-1043},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Lv, Zeqiong; Qian, Chao; Sun, Yanan
A First Step Towards Runtime Analysis of Evolutionary Neural Architecture Search Technical Report
2024.
@techreport{lv2024step,
title = {A First Step Towards Runtime Analysis of Evolutionary Neural Architecture Search},
author = {Zeqiong Lv and Chao Qian and Yanan Sun},
url = {https://arxiv.org/abs/2401.11712},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Wang, Xinlei; He, Mingshu; Wang, Jiaxuan; Wang, Xiaojuan
Towards Efficient Neural Networks Through Predictor-Assisted NSGA-III for Anomaly Traffic Detection of IoT Journal Article
In: IEEE Transactions on Cognitive Communications and Networking, pp. 1-1, 2024.
@article{10403928,
title = {Towards Efficient Neural Networks Through Predictor-Assisted NSGA-III for Anomaly Traffic Detection of IoT},
author = {Xinlei Wang and Mingshu He and Jiaxuan Wang and Xiaojuan Wang},
url = {https://ieeexplore.ieee.org/abstract/document/10403928},
doi = {10.1109/TCCN.2024.3355433},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Cognitive Communications and Networking},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fuentes-Tomás, José Antonio; Acosta-Mesa, Héctor Gabriel; Mezura-Montes, Efrén; Jiménez, Rodolfo Hernandez
Neural Architecture Search for Placenta Segmentation in 2D Ultrasound Images Proceedings Article
In: Calvo, Hiram; Martínez-Villaseñor, Lourdes; Ponce, Hiram; Cabada, Ramón Zatarain; Rivera, Martín Montes; Mezura-Montes, Efrén (Ed.): Advances in Computational Intelligence. MICAI 2023 International Workshops, pp. 397–408, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-51940-6.
@inproceedings{10.1007/978-3-031-51940-6_30,
title = {Neural Architecture Search for Placenta Segmentation in 2D Ultrasound Images},
author = {José Antonio Fuentes-Tomás and Héctor Gabriel Acosta-Mesa and Efrén Mezura-Montes and Rodolfo Hernandez Jiménez},
editor = {Hiram Calvo and Lourdes Martínez-Villaseñor and Hiram Ponce and Ramón Zatarain Cabada and Martín Montes Rivera and Efrén Mezura-Montes},
url = {https://link.springer.com/chapter/10.1007/978-3-031-51940-6_30#citeas},
isbn = {978-3-031-51940-6},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Advances in Computational Intelligence. MICAI 2023 International Workshops},
pages = {397–408},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Monitoring the placenta during pregnancy can lead to early diagnosis of anomalies by observing their characteristics, such as size, shape, and location. Ultrasound is a popular medical imaging technique used in placenta monitoring, whose advantages include the non-invasive feature, price, and accessibility. However, images from this domain are characterized by their noise. A segmentation system is required to recognize placenta features. U-Net architecture is a convolutional neural network that has become popular in the literature for medical image segmentation tasks. However, this type is a general-purpose network that requires great expertise to design and may only be applicable in some domains. The evolutionary computation overcomes this limitation, leading to the automatic design of convolutional neural networks. This work proposes a U-Net-based neural architecture search algorithm to construct convolutional neural networks applied in the placenta segmentation on 2D ultrasound images. The results show that the proposed algorithm allows a decrease in the number of parameters of U-Net, ranging from 80 to 98%. Moreover, the segmentation performance achieves a competitive level compared to U-Net, with a difference of 0.012 units in the Dice index.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Kumar, Kapil; Verma, Kamal Kant
Comparative study on object detection in visual scenes using deep learning Journal Article
In: World Journal of Biology Pharmacy and Health Sciences Site Logo World Journal of Advanced Engineering Technology and Sciences, 2024.
@article{Kumar-wjaets23a,
title = {Comparative study on object detection in visual scenes using deep learning},
author = {Kapil Kumar and Kamal Kant Verma
},
url = {https://wjaets.com/content/comparative-study-object-detection-visual-scenes-using-deep-learning},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {World Journal of Biology Pharmacy and Health Sciences Site Logo World Journal of Advanced Engineering Technology and Sciences},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mehrabian, Amir; Sabbaghian, Maryam; Yanikomeroglu, Halim
RL-Based Hyperparameter Selection for Spectrum Sensing With CNNs Journal Article
In: IEEE Transactions on Communications, pp. 1-1, 2024.
@article{10399938,
title = {RL-Based Hyperparameter Selection for Spectrum Sensing With CNNs},
author = {Amir Mehrabian and Maryam Sabbaghian and Halim Yanikomeroglu},
url = {https://ieeexplore.ieee.org/abstract/document/10399938},
doi = {10.1109/TCOMM.2024.3354204},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Communications},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Anagolum, Sashwat; Alavisamani, Narges; Das, Poulami; Qureshi, Moinuddin; Kessler, Eric; Shi, Yunong
Élivágar: Efficient Quantum Circuit Search for Classification Technical Report
2024.
@techreport{anagolum2024elivagar,
title = {Élivágar: Efficient Quantum Circuit Search for Classification},
author = {Sashwat Anagolum and Narges Alavisamani and Poulami Das and Moinuddin Qureshi and Eric Kessler and Yunong Shi},
url = {https://arxiv.org/abs/2401.09393},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Yang, An; Liu, Ying; Li, Chunguang; Ren, Qinyuan
Deeply Supervised Block-Wise Neural Architecture Search Journal Article
In: IEEE Trans Neural Netw Learn Syst , 2024.
@article{Yang-itnnls24a,
title = { Deeply Supervised Block-Wise Neural Architecture Search },
author = {An Yang and Ying Liu and Chunguang Li and Qinyuan Ren
},
url = {https://pubmed.ncbi.nlm.nih.gov/38231812/},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = { IEEE Trans Neural Netw Learn Syst },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Qiao, Ye; Xu, Haocheng; Zhang, Yifan; Huang, Sitao
MicroNAS: Zero-Shot Neural Architecture Search for MCUs Technical Report
2024.
@techreport{qiao2024micronas,
title = {MicroNAS: Zero-Shot Neural Architecture Search for MCUs},
author = {Ye Qiao and Haocheng Xu and Yifan Zhang and Sitao Huang},
url = {https://arxiv.org/abs/2401.08996},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Raja, Muhammad Adil; Loughran, Róisín; Mccaffery, Fergal
Performance Analysis of YOLO-NAS SOTA Models on CAL Tool Detection Journal Article
In: 2024.
@article{Raja_2024,
title = {Performance Analysis of YOLO-NAS SOTA Models on CAL Tool Detection},
author = {Muhammad Adil Raja and Róisín Loughran and Fergal Mccaffery},
url = {http://dx.doi.org/10.36227/techrxiv.170474405.56692658/v1},
doi = {10.36227/techrxiv.170474405.56692658/v1},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Liu, Jianwei Zhao Jie Li Xin
Evolutionary Neural Architecture Search and Its Applications in Healthcare Journal Article
In: Computer Modeling in Engineering & Sciences, vol. 139, no. 1, pp. 143–185, 2024, ISSN: 1526-1506.
@article{cmes.2023.030391,
title = {Evolutionary Neural Architecture Search and Its Applications in Healthcare},
author = {Jianwei Zhao Jie Li Xin Liu},
url = {http://www.techscience.com/CMES/v139n1/55101},
doi = {10.32604/cmes.2023.030391},
issn = {1526-1506},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Computer Modeling in Engineering & Sciences},
volume = {139},
number = {1},
pages = {143–185},
abstract = {Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the use of EAs for NAS. In this paper, we summarize and explore the use of EAs for NAS, as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods. NAS performs well in healthcare applications, such as medical image analysis, classification of disease diagnosis, and health monitoring. EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task. Deep neural network has been successfully used in healthcare, but it lacks interpretability. Medical data is highly sensitive, and privacy leaks are frequently reported in the healthcare industry. To solve these problems, in healthcare, we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection. Moreover, we also point out future research directions for evolutionary NAS. Overall, for researchers who want to use EAs to optimize NNs in healthcare, we analyze the advantages and disadvantages of doing so to provide detailed guidance, and propose an interpretable privacy-preserving framework for healthcare applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Vu, Thanh
TOWARD EFFICIENT AND ROBUST COMPUTER VISION FOR LARGE-SCALE EDGE APPLICATIONS PhD Thesis
2024.
@phdthesis{Yu-PhD23a,
title = {TOWARD EFFICIENT AND ROBUST COMPUTER VISION FOR LARGE-SCALE EDGE APPLICATIONS},
author = {Thanh Vu },
url = {https://www.proquest.com/openview/9bb8722acfbd4abf1c7d8317a9d79342/1?pq-origsite=gscholar&cbl=18750&diss=y},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Feng, Jian; He, Yajie; Pan, Yuhan; Zhou, Zhipeng; Chen, Si; Gong, Wei
Enhancing Fitness Evaluation in Genetic Algorithm-Based Architecture Search for AI-Aided Financial Regulation Journal Article
In: IEEE Transactions on Evolutionary Computation, pp. 1-1, 2024.
@article{10388040,
title = {Enhancing Fitness Evaluation in Genetic Algorithm-Based Architecture Search for AI-Aided Financial Regulation},
author = {Jian Feng and Yajie He and Yuhan Pan and Zhipeng Zhou and Si Chen and Wei Gong},
url = {https://ieeexplore.ieee.org/abstract/document/10388040},
doi = {10.1109/TEVC.2024.3352239},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Evolutionary Computation},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Dolatabadi, Amirhossein; Abdeltawab, Hussein Hassan; Mohamed, Yasser Abdel-Rady I.
In: IEEE Access, vol. 12, pp. 7674–7688, 2024.
@article{DBLP:journals/access/DolatabadiAM24,
title = {SFNAS-DDPG: A Biomass-Based Energy Hub Dynamic Scheduling Approach via Connecting Supervised Federated Neural Architecture Search and Deep Deterministic Policy Gradient},
author = {Amirhossein Dolatabadi and Hussein Hassan Abdeltawab and Yasser Abdel-Rady I. Mohamed},
url = {https://doi.org/10.1109/ACCESS.2024.3352032},
doi = {10.1109/ACCESS.2024.3352032},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Access},
volume = {12},
pages = {7674–7688},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gupta, Vyom Kumar; Lalwani, Suraj Kumar; Bhati, Gaurav Singh; Prakash, Surya; Sunny,
Bayesian Optimization Based Neural Architecture Search for Classification of Gases/Odors Mixtures Journal Article
In: IEEE Sensors Journal, pp. 1-1, 2024.
@article{10388255,
title = {Bayesian Optimization Based Neural Architecture Search for Classification of Gases/Odors Mixtures},
author = {Vyom Kumar Gupta and Suraj Kumar Lalwani and Gaurav Singh Bhati and Surya Prakash and Sunny},
url = {https://ieeexplore.ieee.org/abstract/document/10388255},
doi = {10.1109/JSEN.2024.3349862},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Sensors Journal},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Yang, Dong; Roth, Holger R.; Wang, Xiaosong; Xu, Ziyue; Xu, Daguang
In: Zhou, S. Kevin; Greenspan, Hayit; Shen, Dinggang (Ed.): Deep Learning for Medical Image Analysis (Second Edition), pp. 281-298, Academic Press, 2024, ISBN: 978-0-323-85124-4.
@incollection{YANG2024281,
title = {Chapter 10 - Dynamic inference using neural architecture search in medical image segmentation: From a novel adaptation perspective},
author = {Dong Yang and Holger R. Roth and Xiaosong Wang and Ziyue Xu and Daguang Xu},
editor = {S. Kevin Zhou and Hayit Greenspan and Dinggang Shen},
url = {https://www.sciencedirect.com/science/article/pii/B9780323851244000210},
doi = {https://doi.org/10.1016/B978-0-32-385124-4.00021-0},
isbn = {978-0-323-85124-4},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Deep Learning for Medical Image Analysis (Second Edition)},
pages = {281-298},
publisher = {Academic Press},
edition = {Second Edition},
series = {The MICCAI Society book Series},
abstract = {Data inconsistency in medical imaging acquisition has been existing for decades, which creates difficulties when researchers adopt learning-based processing methods to unknown data. This issue is mostly caused by medical image scanners from different vendors, inconsistent scanning protocols, anatomy discrepancy among populations, environmental artifacts or other related factors. For instance, large appearance variance may exist in 3D T2-weighted brain MRI from different institutions or hospitals, even scanned with the same scanning protocols. Meanwhile, the data inconsistency downgrades the performance of machine learning models for medical image processing, such as organ or tumor segmentation, when models face unknown data at inference with pre-trained models. To alleviate the potential side effects caused by the data inconsistency, we propose a novel approach to improve model generalizability and transferability for unknown data leveraging the concepts from neural architecture search. We build a general “super-net” enabling multiple candidate modules in parallel to represent multi-scale contextual features at different network levels, respectively. After the training of the super-net is accomplished, a unique and optimal architecture for each data point is determined with guidance of additional model constraints at inference. We also propose a novel path sampling strategy to enable “fair” model training. Our experiments show that the proposed approach has clear advantages over the conventional neural network deployment in terms of segmentation performance and generalization in the unknown images.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Fuentes-Tomás, José-Antonio; Mezura-Montes, Efrén; Acosta-Mesa, Héctor-Gabriel; Márquez-Grajales, Aldo
Tree-Based Codification in Neural Architecture Search for Medical Image Segmentation Journal Article
In: IEEE Transactions on Evolutionary Computation, pp. 1-1, 2024.
@article{10391062,
title = {Tree-Based Codification in Neural Architecture Search for Medical Image Segmentation},
author = {José-Antonio Fuentes-Tomás and Efrén Mezura-Montes and Héctor-Gabriel Acosta-Mesa and Aldo Márquez-Grajales},
url = {https://ieeexplore.ieee.org/abstract/document/10391062},
doi = {10.1109/TEVC.2024.3353182},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Evolutionary Computation},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wang, Yan; Zhen, Liangli; Zhang, Jianwei; Li, Miqing; Zhang, Lei; Wang, Zizhou; Feng, Yangqin; Xue, Yu; Wang, Xiao; Chen, Zheng; Luo, Tao; Goh, Rich Siow Mong; Liu, Yong
MedNAS: Multi-Scale Training-Free Neural Architecture Search for Medical Image Analysis Journal Article
In: IEEE Transactions on Evolutionary Computation, pp. 1-1, 2024.
@article{10391077,
title = {MedNAS: Multi-Scale Training-Free Neural Architecture Search for Medical Image Analysis},
author = {Yan Wang and Liangli Zhen and Jianwei Zhang and Miqing Li and Lei Zhang and Zizhou Wang and Yangqin Feng and Yu Xue and Xiao Wang and Zheng Chen and Tao Luo and Rich Siow Mong Goh and Yong Liu},
url = {https://ieeexplore.ieee.org/abstract/document/10391077/authors#authors},
doi = {10.1109/TEVC.2024.3352641},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Evolutionary Computation},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wu, Haihang; Wang, Wei; Malepathirana, Tamasha; Senanayake, Damith A.; Oetomo, Denny; Halgamuge, Saman K.
When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks Technical Report
2024.
@techreport{DBLP:journals/corr/abs-2401-03104,
title = {When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks},
author = {Haihang Wu and Wei Wang and Tamasha Malepathirana and Damith A. Senanayake and Denny Oetomo and Saman K. Halgamuge},
url = {https://doi.org/10.48550/arXiv.2401.03104},
doi = {10.48550/ARXIV.2401.03104},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {CoRR},
volume = {abs/2401.03104},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Şahin, Emrullah; Özdemir, Durmuş; Temurtaş, Hasan
Multi-objective optimization of ViT architecture for efficient brain tumor classification Journal Article
In: Biomedical Signal Processing and Control, vol. 91, pp. 105938, 2024, ISSN: 1746-8094.
@article{SAHIN2024105938,
title = {Multi-objective optimization of ViT architecture for efficient brain tumor classification},
author = {Emrullah Şahin and Durmuş Özdemir and Hasan Temurtaş},
url = {https://www.sciencedirect.com/science/article/pii/S174680942301371X},
doi = {https://doi.org/10.1016/j.bspc.2023.105938},
issn = {1746-8094},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Biomedical Signal Processing and Control},
volume = {91},
pages = {105938},
abstract = {This study presents an advanced approach to optimizing the Vision Transformer (ViT) network for brain tumor classification in 2D MRI images, utilizing Bayesian Multi-Objective (BMO) optimization techniques. Rather than merely addressing the limitations of the standard ViT model, our objective was to enhance its overall efficiency and effectiveness. The application of BMO enabled us to fine-tune the architectural parameters of the ViT network, resulting in a model that was not only twice as fast but also four times smaller in size compared to the original. In terms of performance, the optimized ViT model achieved notable improvements, with a 1.48 % increase in validation accuracy, a 3.23 % rise in the F1-score, and a 3.36 % improvement in precision. These substantial enhancements highlight the potential of integrating BMO with visual transformer-based models, suggesting a promising direction for future research in achieving high efficiency and accuracy in complex classification tasks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Yan, Shen; Meng, Qingyan; Xiao, Mingqing; Wang, Yisen; Lin, Zhouchen
Sampling complex topology structures for spiking neural networks Journal Article
In: Neural Networks, vol. 172, pp. 106121, 2024, ISSN: 0893-6080.
@article{YAN2024106121,
title = {Sampling complex topology structures for spiking neural networks},
author = {Shen Yan and Qingyan Meng and Mingqing Xiao and Yisen Wang and Zhouchen Lin},
url = {https://www.sciencedirect.com/science/article/pii/S0893608024000352},
doi = {https://doi.org/10.1016/j.neunet.2024.106121},
issn = {0893-6080},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Neural Networks},
volume = {172},
pages = {106121},
abstract = {Spiking Neural Networks (SNNs) have been considered a potential competitor to Artificial Neural Networks (ANNs) due to their high biological plausibility and energy efficiency. However, the architecture design of SNN has not been well studied. Previous studies either use ANN architectures or directly search for SNN architectures under a highly constrained search space. In this paper, we aim to introduce much more complex connection topologies to SNNs to further exploit the potential of SNN architectures. To this end, we propose the topology-aware search space, which is the first search space that enables a more diverse and flexible design for both the spatial and temporal topology of the SNN architecture. Then, to efficiently obtain architecture from our search space, we propose the spatio-temporal topology sampling (STTS) algorithm. By leveraging the benefits of random sampling, STTS can yield powerful architecture without the need for an exhaustive search process, making it significantly more efficient than alternative search strategies. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate the effectiveness of our method. Notably, we obtain 70.79% top-1 accuracy on ImageNet with only 4 time steps, 1.79% higher than the second best model. Our code is available under https://github.com/stiger1000/Random-Sampling-SNN.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Shin, Iksoo; Cho, Changsik; Kim, Seon-Tae
Method for Expanding Search Space With Hybrid Operations in DynamicNAS Journal Article
In: IEEE Access, vol. 12, pp. 10242–10253, 2024.
@article{DBLP:journals/access/ShinCK24,
title = {Method for Expanding Search Space With Hybrid Operations in DynamicNAS},
author = {Iksoo Shin and Changsik Cho and Seon-Tae Kim},
url = {https://doi.org/10.1109/ACCESS.2024.3350732},
doi = {10.1109/ACCESS.2024.3350732},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Access},
volume = {12},
pages = {10242–10253},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Xue, Yu; Han, Xiaolong; Wang, Zehong
Self-Adaptive Weight Based on Dual-Attention for Differentiable Neural Architecture Search Journal Article
In: IEEE Transactions on Industrial Informatics, pp. 1-10, 2024.
@article{10384749,
title = {Self-Adaptive Weight Based on Dual-Attention for Differentiable Neural Architecture Search},
author = {Yu Xue and Xiaolong Han and Zehong Wang},
url = {https://ieeexplore.ieee.org/abstract/document/10384749},
doi = {10.1109/TII.2023.3348843},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Transactions on Industrial Informatics},
pages = {1-10},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Jo, Haesung; Joo, Changhee
AutoGAN-DSP: Stabilizing GAN architecture search with deterministic score predictors Journal Article
In: Neurocomputing, pp. 127187, 2024, ISSN: 0925-2312.
@article{JO2024127187,
title = {AutoGAN-DSP: Stabilizing GAN architecture search with deterministic score predictors},
author = {Haesung Jo and Changhee Joo},
url = {https://www.sciencedirect.com/science/article/pii/S0925231223013103},
doi = {https://doi.org/10.1016/j.neucom.2023.127187},
issn = {0925-2312},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Neurocomputing},
pages = {127187},
abstract = {Generative Adversarial Network (GAN) has been widely used in many research areas of computer vision, anomaly detection, translation, optimal control, etc. However, in most cases, its network architectures have been hand-picked based on human experiences. To this end, neural Architecture Search (NAS) that automatically finds architectures have attracted much attention to automate the architecture search. In this work, we show that the NAS for Generative Adversarial Network (GAN), also denoted Generative Adversarial Neural Architecture Search (GANAS), often suffers from unstable search due to its innate randomness in the performance evaluation process. We address the stability issue by introducing deterministic score predictors and develop a unified framework to simultaneously conduct the architecture search and the predictor training. Further we develop a novel 2-phase architecture and parameter selection process to balance computational cost and architecture performance. Through extensive experiments, we demonstrate that our proposed AutoGAN-DSP outperforms other RL-based GANAS schemes as well as stabilizing the search performance. Our code and datasets are available on GitHub (https://github.com/APinCan/GAN_Architecture_Search_with_Predictors).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lin, Tzu-Han; Wang, How-Shing; Weng, Hao-Yung; Peng, Kuang-Chen; Chen, Zih-Ching; Lee, Hung-yi
PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques Miscellaneous
2024.
@misc{lin2024peft,
title = {PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques},
author = {Tzu-Han Lin and How-Shing Wang and Hao-Yung Weng and Kuang-Chen Peng and Zih-Ching Chen and Hung-yi Lee},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Gao, Jianliang; Wu, Zhenpeng; Al-Sabri, Raeed; Oloulade, Babatounde Moctard; Chen, Jiamin
AutoDDI: Drug–Drug Interaction Prediction With Automated Graph Neural Network Journal Article
In: IEEE Journal of Biomedical and Health Informatics, pp. 1-12, 2024.
@article{10380606,
title = {AutoDDI: Drug–Drug Interaction Prediction With Automated Graph Neural Network},
author = {Jianliang Gao and Zhenpeng Wu and Raeed Al-Sabri and Babatounde Moctard Oloulade and Jiamin Chen},
doi = {10.1109/JBHI.2024.3349570},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1-12},
keywords = {},
pubstate = {published},
tppubtype = {article}
}