Literature on Neural Architecture Search

@inproceedings{10.1007/978-981-19-3387-5_117,

title = {Continuous Evolution for Efficient Neural Architecture Search Based on Improved NSGA-III Algorithm},

author = {Ziyan Wang and Feng Qi and Liming Zou},

editor = {Jiande Sun and Yue Wang and Mengyao Huo and Lexi Xu},

isbn = {978-981-19-3387-5},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {Signal and Information Processing, Networking and Computers},

pages = {979--986},

publisher = {Springer Nature Singapore},

address = {Singapore},

abstract = {Improved Continuous Evolution for Efficient Neural Architecture Search method (I-CARS) is proposed to solve multi-objective optimization problems (MOPs). Seeking to improve the convergence and search accuracy, two modifications of the non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) were made to replace pNSGA-III in CARS, including the penalty-based boundary intersection distance (PBI distance) and the selection-and-elimination operators. First, the perpendicular distance from solutions to reference lines was replaced by the PBI distance in the offspring selection stage phase, which can add the convergence information. Second, individuals in the population were selected or eliminated by niche count and PBI distance. Better performing individuals were selected to become the next generation, while poorly performing individuals were eliminated. The convergence and diversity of the population can be balanced by adding the selection-and-elimination operator. Experiments were conducted on Vega pipeline, and I-CARS achieves 3.41% test error on CIFAR10, the results indicated that the accuracy and convergence of I-CARS are enhanced compared to CARS.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{KeEAST2022,

title = {EAST: An Enhanced Automated Machine Learning Library for Spatio-Temporal Forecasting},

author = {Songyu ke and Li Song and Kainan Bao and Zheyi Pan and Junbo Zhang and Yu Zheng},

year  = {2022},

date = {2022-08-15},

urldate = {2022-08-15},

booktitle = {DeepSpatial ’22},

address = {Washington, DC, USA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{JeongDyNNICML2022,

title = {Neural Architecture Search with Loss Flatness-aware Measure},

author = {Joonhyun Jeong and Joonsang Yu and Dongyoon Han and YoungJoon Yoo},

url = {https://dynn-icml2022.github.io/papers/paper_11.pdf},

year  = {2022},

date = {2022-07-26},

urldate = {2022-07-26},

booktitle = {DyNN workshop at the 39th International Conference on Machine Learning},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{SuchopárováAML22,

title = {Graph Embedding for Neural Architecture Search with Input-Output Information},

author = {Gabriela Suchopárová and Roman Neruda},

url = {https://automl.cc/wp-content/uploads/2022/07/graph_embedding_for_neural_arc.pdf},

year  = {2022},

date = {2022-07-21},

urldate = {2022-07-21},

booktitle = {1st International Conference on, Automated Machine Learning, Late-Breaking Workshop},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{GastelAML22,

title = {Regularized Meta-Learning for Neural Architecture Search},

author = {Rob van Gastel and Joaquin Vanschoren

},

url = {https://automl.cc/wp-content/uploads/2022/07/regularized_meta_learning_for_.pdf},

year  = {2022},

date = {2022-07-21},

urldate = {2022-07-21},

booktitle = {1st International Conference on, Automated Machine Learning, Late-Breaking Workshop},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chatzimichailidisaml22,

title = {GSparsity: Unifying Network Pruning and Neural Architecture Search by Group Sparsity},

author = {Avraam Chatzimichailidis and Arber Zela and Janis Keuper and Yang Yang

},

url = {https://automl.cc/wp-content/uploads/2022/07/gsparsity_unifying_network_pru.pdf},

year  = {2022},

date = {2022-07-21},

urldate = {2022-07-21},

booktitle = {1st International Conference on, Automated Machine Learning, Late-Breaking Workshop},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {Searching Efficient Dynamic Graph CNN for Point Cloud Processing},

author = {Panyue Chen and Rui Wang and Ping Zhao and Guanming Liu and Zhihua Wei

},

url = {https://automl.cc/wp-content/uploads/2022/07/searching_efficient_dynamic_gr.pdf},

year  = {2022},

date = {2022-07-21},

urldate = {2022-07-21},

booktitle = {1st International Conference on Automated Machine Learning, Late-Breaking Workshop},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{LiIjCAI2022,

title = {Bi-fidelity Multi-objective Neural Architecture Search for Adversarial Robustness with Surrogate as a Helper-objective},

author = {Jia Liu and Yaochu Jin},

url = {https://federated-learning.org/fl-ijcai-2022/Papers/FL-IJCAI-22_paper_22.pdf},

year  = {2022},

date = {2022-07-01},

urldate = {2022-07-01},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pmlr-v162-sun22c,

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

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

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

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

year  = {2022},

date = {2022-07-01},

urldate = {2022-07-01},

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

volume = {162},

pages = {20810--20826},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

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

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{pmlr-v162-sun22cb,

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

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

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

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

year  = {2022},

date = {2022-07-01},

urldate = {2022-07-01},

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

volume = {162},

pages = {20810--20826},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

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

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{MarchisioDyNN2022,

title = {HARNAS: Neural Architecture Search Jointly Optimizing for Hardware Efficiency and Adversarial Robustness of Convolutional and Capsule Networks},

author = {Alberto Marchisio and Vojtech Mrazek and Andrea Massa and Beatrice Bussolino and Maurizio Martina and Muhammad Shafique},

url = {https://dynn-icml2022.github.io/papers/paper_18.pdf},

year  = {2022},

date = {2022-07-01},

urldate = {2022-07-01},

booktitle = {DyNN workshop at the 39th International Conference on Machine Learning},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Hendrickx_2022_CVPR,

title = {Hot-Started NAS for Task-Specific Embedded Applications},

author = {Lotte Hendrickx and Wiebe Van Ranst and Toon Goedemé},

url = {https://openaccess.thecvf.com/content/CVPR2022W/NAS/papers/Hendrickx_Hot-Started_NAS_for_Task-Specific_Embedded_Applications_CVPRW_2022_paper.pdf},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},

pages = {1971-1978},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{YueIJCNN2022,

title = {Learning Feature Alignment Architecture for Domain Adaptation},

author = {Zhixiong Yue and Pengxin Guo and Yu Zhang and Jie Liang},

url = {https://yuezhixiong.github.io/Papers/AASPC.pdf},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

booktitle = {IJCNN 2022},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ayman2022,

title = {Neural Architecture and Feature Search for Predicting the Ridership of Public Transportation Routes},

author = {Afiya Ayman and Juan Martinez and Philip Pugliese and Abhishek Dubey and Aron Laszka},

url = {https://aronlaszka.com/papers/ayman2022neural.pdf},

year  = {2022},

date = {2022-05-01},

booktitle = {proceedings of the 8th IEEE International Conference on Smart Computing (SMARTCOMP 2022)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{HeEPJP2022,

title = {Search space pruning for quantum architecture search},

author = {Zhimin He and Junjian Su and Chuangtao Chen and Minghua Pan and Haozhen Situ },

url = {https://link.springer.com/article/10.1140/epjp/s13360-022-02714-7},

year  = {2022},

date = {2022-05-01},

urldate = {2022-05-01},

booktitle = {The European Physical Journal Plus  },

volume = {137},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ZhangMulti2022,

title = {Multi-modal spatio-temporal meteorological forecasting with deep neural network},

author = {Xinbang Zhang and Qizhao Jin and Tingzhao Yu and Shiming Xiang and Qiuming Kuang and Véronique Prinet and Chunhong Pan},

url = {https://pubag.nal.usda.gov/catalog/7745216},

year  = {2022},

date = {2022-05-01},

booktitle = {ISPRS journal of photogrammetry and remote sensing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ZhangMulti2022b,

title = {Multi-modal spatio-temporal meteorological forecasting with deep neural network},

author = {Xinbang Zhang and Qizhao Jin and Tingzhao Yu and Shiming Xiang and Qiuming Kuang and Véronique Prinet and Chunhong Pan},

url = {https://pubag.nal.usda.gov/catalog/7745216},

year  = {2022},

date = {2022-05-01},

urldate = {2022-05-01},

booktitle = {ISPRS journal of photogrammetry and remote sensing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ZhangMulti2022c,

title = {Multi-modal spatio-temporal meteorological forecasting with deep neural network},

author = {Xinbang Zhang and Qizhao Jin and Tingzhao Yu and Shiming Xiang and Qiuming Kuang and Véronique Prinet and Chunhong Pan},

url = {https://pubag.nal.usda.gov/catalog/7745216},

year  = {2022},

date = {2022-05-01},

urldate = {2022-05-01},

booktitle = {ISPRS journal of photogrammetry and remote sensing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{MengIMCIC2022,

title = {Salient Patch Based NAS for Grading of Colorectal Cancer Histology Images},

author = { Hang Meng and Fei Li and Zhen Yang and Qiang Zhu and Chaogang Fan and Shu Zhan },

url = {https://www.iiis.org/DOI2022/ZA922IZ/#/},

year  = {2022},

date = {2022-03-08},

urldate = {2022-03-08},

booktitle = {The 13th International Multi-Conference on Complexity, Informatics and Cybernetics: IMCIC 2022},

volume = {978-1-950492-61-9 },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}