Differentiable neural architecture search in equivalent space with exploration enhancement [PDF]
, 2020 Recent works on One-Shot Neural Architecture Search (NAS) mostly adopt a bilevel optimization scheme to alternatively optimize the supernet weights and architecture parameters after relaxing the discrete search space into a differentiable space. However,
Chang, X +5 more
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Differentiable Neural Architecture Search for High-Dimensional Time Series Forecasting
IEEE Access, 2021 This study applies neural architecture search (NAS) techniques to the modeling of high-dimensional time series data such as multi-variate stock indices.
Renlong Jie, Junbin Gao
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RARTS: An Efficient First-Order Relaxed Architecture Search Method
IEEE Access, 2022 Differentiable architecture search (DARTS) is an effective method for data-driven neural network design based on solving a bilevel optimization problem.
Fanghui Xue, Yingyong Qi, Jack Xin
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Exploring Neural Architecture Search Space via Deep Deterministic Sampling
IEEE Access, 2021 Recent developments in Neural Architecture Search (NAS) resort to training the supernet of a predefined search space with weight sharing to speed up architecture evaluation.
Keith G. Mills +8 more
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NDARTS: A Differentiable Architecture Search Based on the Neumann Series
Algorithms, 2023 Neural architecture search (NAS) has shown great potential in discovering powerful and flexible network models, becoming an important branch of automatic machine learning (AutoML).
Xiaoyu Han +3 more
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U-DARTS: Uniform-space differentiable architecture search [PDF]
, 2023 Differentiable architecture search (DARTS) is an effective neural architecture search algorithm based on gradient descent. However, there are two limitations in DARTS.
Wencong Wang +11 more
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Remaining Useful Life Estimation of Aircraft Engines Using Differentiable Architecture Search
Mathematics, 2022 Prognostics and health management (PHM) applications can prevent engines from potential serious accidents by predicting the remaining useful life (RUL). Recently, data-driven methods have been widely used to solve RUL problems.
Pengli Mao +3 more
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No-reference image quality assessment based on automatic machine learning [PDF]
ITM Web of Conferences, 2022 In different applications in deep learning, due to different required features, it is necessary to design specialized Neural Network structure. However, the design of the structure largely depends on the relevant subject knowledge of researchers and lots
Qian Qi, Sang Qingbing
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Differentiable Quantum Architecture Search [PDF]
, 2021 Quantum architecture search (QAS) is the process of automating architecture engineering of quantum circuits. It has been desired to construct a powerful and general QAS platform which can significantly accelerate current efforts to identify quantum ...
Yao, Hong +3 more
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Neural Architecture Search for Light-weight Medical Image Segmentation Network [PDF]
Jisuanji kexue, 2022 Most of the existing medical image segmentation models with excellent performance are manually designed by domain experts.The design process usually requires a lot of professional knowledge and repeated experiments.In addition,the over complex ...
ZHANG Fu-chang, ZHONG Guo-qiang, MAO Yu-xu
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