Results 61 to 70 of about 75,461 (296)
Progressive Neural Architecture Search [PDF]
, 2018 To appear in ECCV 2018 as oral. The code and checkpoint for PNASNet-5 trained on ImageNet (both Mobile and Large) can now be downloaded from https://github.com/tensorflow/models/tree/master/research/slim#Pretrained. Also see https://github.com/chenxi116/PNASNet.TF for refactored and simplified TensorFlow code; see https://github.com/chenxi116/PNASNet ...
Chenxi Liu 0001 +9 more
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Neural Architecture Search for Spiking Neural Networks
, 2022 Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence ...
Youngeun Kim +4 more
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A survey of neural architecture search
Dianxin kexue, 2019 Recently,deep learning has achieved impressive success on various computer vision tasks.The neural architecture is usually a key factor which directly determines the performance of the deep learning algorithm.The automated neural architecture search ...
Mingjie HE, Jie ZHANG, Shiguang SHAN
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Exploiting Operation Importance for Differentiable Neural Architecture Search
, 2021 Recently, differentiable neural architecture search (NAS) methods have made significant progress in reducing the computational costs of NASs. Existing methods search for the best architecture by choosing candidate operations with higher architecture ...
Kung, Sun-Yuan, Zhou, Yuan, Xie, Xukai
core +1 more source
Array, 2021 Neural architecture search has become an indispensable part of the deep learning field. Modern methods allow to find one of the best performing architectures, or to build one from scratch, but they typically make decisions based on the trained accuracy ...
Ekaterina Gracheva
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BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Over the past half-decade, many methods have been considered for neural architecture search (NAS). Bayesian optimization (BO), which has long had success in hyperparameter optimization, has recently emerged as a very promising strategy for NAS when it is coupled with a neural predictor.
Colin White +2 more
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EGNAS: Efficient Graph Neural Architecture Search Through Evolutionary Algorithm
MathematicsThe primary objective of our research is to enhance the efficiency and effectiveness of Neural Architecture Search (NAS) with regard to Graph Neural Networks (GNNs).
Younkyung Jwa +2 more
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Learning a Unified Latent Space for NAS: Toward Leveraging Structural and Symbolic Information
IEEE Access, 2022 Automatically designing neural architectures, i.e., NAS (Neural Architecture Search), is a promising path in machine learning. However, the main challenge for NAS algorithms is to reduce the considerable elapsed time to evaluate a proposed network.
Saeedeh Eslami +2 more
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Multi-Relational Graph Neural Architecture Search with Fine-grained Message Passing
, 2022 Graph neural architecture search (NAS) has gained great popularity in automatically designing powerful graph neural networks (GNNs) with superior learning abilities, significantly relieving human effort and expertise reliance.
Zhou, C +5 more
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KT-NAS: Knowledge Transfer for Efficient Neural Architecture Search
Applied SciencesPre-trained models have played important roles in many tasks, such as domain adaptation and out-of-distribution generalization, by transferring matured knowledge.
Linh-Tam Tran +3 more
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