Results 51 to 60 of about 7,839,223 (287)

SimQ-NAS: Simultaneous Quantization Policy and Neural Architecture Search

CoRR, 2023
Recent one-shot Neural Architecture Search algorithms rely on training a hardware-agnostic super-network tailored to a specific task and then extracting efficient sub-networks for different hardware platforms. Popular approaches separate the training of super-networks from the search for sub-networks, often employing predictors to alleviate the ...
Sharath Nittur Sridhar, Maciej Szankin, Fang Chen, Sairam Sundaresan, Anthony Sarah   +4 more
openaire   +3 more sources

Pareto-optimal progressive neural architecture search [PDF]

, 2021
Neural Architecture Search (NAS) is the process of automating architecture engineering, searching for the best deep learning configuration. One of the main NAS approaches proposed in the literature, Progressive Neural Architecture Search (PNAS), seeks ...
Eugenio Lomurno, Stefano Samele, M. Matteucci, Matteo Matteucci, E. Lomurno, D. Ardagna, Danilo Ardagna, S. Samele   +7 more
core   +1 more source

Gradient-Based Neural Architecture Search: A Comprehensive Evaluation

Machine Learning and Knowledge Extraction, 2023
One of the challenges in deep learning involves discovering the optimal architecture for a specific task. This is effectively tackled through Neural Architecture Search (NAS).
Sarwat Ali, M. Arif Wani
doaj   +1 more source

HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search

CoRR, 2021
Realistic use of neural networks often requires adhering to multiple constraints on latency, energy and memory among others. A popular approach to find fitting networks is through constrained Neural Architecture Search (NAS), however, previous methods enforce the constraint only softly.
Niv Nayman, Yonathan Aflalo, Asaf Noy, Lihi Zelnik   +3 more
openaire   +4 more sources

Neural architecture search for medicine: A survey

Informatics in Medicine Unlocked
In this article we examined research on using neural architecture search (NAS) in medical applications, prompted by the current shortage of health care professionals relative to patient volumes. We explored the current state of NAS development in various
Sinee Chaiyarin, Napassorn Rojbundit, Panichanok Piyabenjarad, Pimpattra Limpitigranon, Siraprapa Wisitthipakdeekul, Pawaree Nonthasaen, Paniti Achararit   +6 more
doaj   +1 more source

FastDARTSDet: Fast Differentiable Architecture Joint Search on Backbone and FPN for Object Detection

Applied Sciences, 2022
Neural architecture search (NAS) is a popular branch of automatic machine learning (AutoML), which aims to search for efficient network structures.
Chunxian Wang, Xiaoxing Wang, Yiwen Wang, Shengchao Hu, Hongyang Chen, Xuehai Gu, Junchi Yan, Tao He   +7 more
doaj   +1 more source

Federated Neural Architecture Search [PDF]

, 2022
To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data. However, training over decentralized data makes the design of neural architecture quite difficult as it ...
Bian, Kaigui, Wang, Shangguang, Zhao, Yuxin, Yuan, Jinliang, Xu, Mengwei, Liu, Xuanzhe, Huang, Gang   +6 more
core   +1 more source

Cross-Domain Scene Classification Based on a Spatial Generalized Neural Architecture Search for High Spatial Resolution Remote Sensing Images

Remote Sensing, 2021
By labelling high spatial resolution (HSR) images with specific semantic classes according to geographical properties, scene classification has been proven to be an effective method for HSR remote sensing image semantic interpretation.
Yuling Chen, Wentao Teng, Zhen Li, Qiqi Zhu, Qingfeng Guan   +4 more
doaj   +1 more source

SEval-NAS: A Search-Agnostic Evaluation for Neural Architecture Search

Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing
To be published in the Proceedings of The 41st ACM/SIGAPP Symposium on Applied Computing (SAC26)
Atah Nuh Mih, Jianzhou Wang, Hung Truong Thanh Nguyen, Hung Cao   +3 more
openaire   +2 more sources

A survey on computationally efficient neural architecture search

Journal of Automation and Intelligence, 2022
Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks ...
Shiqing Liu, Haoyu Zhang, Yaochu Jin
doaj   +1 more source