Applied Sciences, 2023 In the realm of machine learning, the optimization of hyperparameters and the design of neural architectures entail laborious and time-intensive endeavors.
Jiyong Shin, Kyongseok Park, Dae-Ki Kang
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Automated deep learning architecture design using differentiable architecture search (DARTS) [PDF]
, 2019 Creating neural networks by hand is a slow trial-and-error based process. Designing new architectures similar to GoogleNet or FractalNets, which use repeated tree-based structures, is highly likely to be inefficient and sub-optimal because of the large number of possibilities for composing such structures.
Sharma, Kartikay, author +3 more
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Relax DARTS: Relaxing the Constraints of Differentiable Architecture Search for Eye Movement Recognition [PDF]
Accepted By CCBR ...
Hongyu Zhu +5 more
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LMD-DARTS: Low-Memory, Densely Connected, Differentiable Architecture Search
ElectronicsNeural network architecture search (NAS) technology is pivotal for designing lightweight convolutional neural networks (CNNs), facilitating the automatic search for network structures without requiring extensive prior knowledge. However, NAS is resource-intensive, consuming significant computational power and time due to the evaluation of numerous ...
Zhongnian Li +5 more
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Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search [PDF]
2021 International Joint Conference on Neural Networks (IJCNN), 2021 Arrhythmia is a cardiovascular disease that manifests irregular heartbeats. In arrhythmia detection, the electrocardiogram (ECG) signal is an important diagnostic technique. However, manually evaluating ECG signals is a complicated and time-consuming task.
Jindi Lv +4 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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iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients [PDF]
, 2021 Differentiable ARchiTecture Search(DARTS) has recently become the mainstream in the neural architecture search (NAS) due to its efficiency and simplicity.
Zhang, Miao +5 more
core +5 more sources
Differentiable Architecture Search Algorithm Based on Global Comparison
IEEE Access, 2023 Manually building neural network models is a great test of researchers’ knowledge reserves, so using Neural Architecture Search (NAS) to automatically construct neural networks is becoming increasingly popular.
Xianglun Zeng, Hongxiang Xiao
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$\beta$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 CVPR2022
Peng Ye 0006 +5 more
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MSR-DARTS: Minimum Stable Rank of Differentiable Architecture Search
2022 International Joint Conference on Neural Networks (IJCNN), 2022 In neural architecture search (NAS), differentiable architecture search (DARTS) has recently attracted much attention due to its high efficiency. It defines an over-parameterized network with mixed edges, each of which represents all operator candidates, and jointly optimizes the weights of the network and its architecture in an alternating manner ...
Kengo Machida +3 more
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