Results 21 to 30 of about 75,461 (296)
Neural Predictor for Neural Architecture Search [PDF]
, 2020 Neural Architecture Search methods are effective but often use complex algorithms to come up with the best architecture. We propose an approach with three basic steps that is conceptually much simpler. First we train N random architectures to generate N (architecture, validation accuracy) pairs and use them to train a regression model that predicts ...
Wei Wen 0003 +5 more
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A Fast and Progressive Convolutional Neural Architecture Search Algorithm [PDF]
Jisuanji gongcheng, 2022 Manually designing a Convolutional Neural Network(CNN) architecture is extremely difficult and requires a high level of professionalism.The gradient differentiable search is fast and efficient.However, this method has some drawbacks, such as a large gap ...
ZHAO Liang, FANG Wei
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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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Neural architecture search of echocardiography view classifiers [PDF]
, 2021 Purpose: Echocardiography is the most commonly used modality for assessing the heart in clinical practice. In an echocardiographic exam, an ultrasound probe samples the heart from different orientations and positions, thereby creating different ...
Matthew J. Shun-Shin +29 more
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POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique [PDF]
, 2022 Automating the research for the best neural network model is a task that has gained more and more relevance in the last few years. In this context, Neural Architecture Search (NAS) represents the most effective technique whose results rival the state of ...
Eugenio Lomurno +4 more
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Neural Architecture Search with Random Labels [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 In this paper, we investigate a new variant of neural architecture search (NAS) paradigm -- searching with random labels (RLNAS). The task sounds counter-intuitive for most existing NAS algorithms since random label provides few information on the performance of each candidate architecture.
Xuanyang Zhang +3 more
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Neural Architecture Search and Hardware Accelerator Co-Search: A Survey
IEEE Access, 2021 Deep neural networks (DNN) are now dominating in the most challenging applications of machine learning. As DNNs can have complex architectures with millions of trainable parameters (the so-called weights), their design and training are difficult even for
Lukas Sekanina
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Neural Architecture Search for Inversion
CoRR, 2022 Over the year, people have been using deep learning to tackle inversion problems, and we see the framework has been applied to build relationship between recording wavefield and velocity (Yang et al., 2016). Here we will extend the work from 2 perspectives, one is deriving a more appropriate loss function, as we now, pixel-2-pixel comparison might not ...
Cheng Zhan +4 more
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Evolving Search Space for Neural Architecture Search [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 Accepted for publication at the 2021 International Conference on Computer Vision (ICCV 2021)
Yuanzheng Ci +5 more
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Neural Architecture Search Benchmarks: Insights and Survey
IEEE Access, 2023 Neural Architecture Search (NAS), a promising and fast-moving research field, aims to automate the architectural design of Deep Neural Networks (DNNs) to achieve better performance on the given task and dataset.
Krishna Teja Chitty-Venkata +3 more
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