Results 11 to 20 of about 6,198,210 (289)
Graph Neural Architecture Search [PDF]
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020 Graph neural networks (GNNs) emerged recently as a powerful tool for analyzing non-Euclidean data such as social network data. Despite their success, the design of graph neural networks requires heavy manual work and domain knowledge.
Zhou, C +9 more
core +3 more sources
Differentiable Neural Architecture, Mixed Precision and Accelerator Co-Search [PDF]
IEEE Access, 2023 Quantization, effective Neural Network architecture, and efficient accelerator hardware are three important design paradigms to maximize accuracy and efficiency.
Krishna Teja Chitty-Venkata +4 more
doaj +3 more sources
Binarized Neural Architecture Search
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Neural architecture search (NAS) can have a significant impact in computer vision by automatically designing optimal neural network architectures for various tasks.
Ji, Rongrong +6 more
core +5 more sources
Contrastive Neural Architecture Search with Neural Architecture Comparators [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Accpeted by CVPR 2021.
Yaofo Chen +6 more
openaire +3 more sources
CommGNAS: Unsupervised Graph Neural Architecture Search for Community Detection [PDF]
, 2023 Graph neural architecture search (GNAS) has been successful in many supervised learning tasks, such as node classification, graph classification, and link prediction.
Raeed Al-Sabri +13 more
core +1 more source
Effective, Efficient and Robust Neural Architecture Search [PDF]
, 2023 Designing neural network architecture for embedded devices is practical but challenging because the models are expected to be not only accurate but also enough lightweight and robust.
Yue, Z, Lin, B, Liang, C, Zhang, Y
core +1 more source
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
core +4 more sources
Auto-GNN: Neural architecture search of graph neural networks
Frontiers in Big Data, 2022 Graph neural networks (GNNs) have been widely used in various graph analysis tasks. As the graph characteristics vary significantly in real-world systems, given a specific scenario, the architecture parameters need to be tuned carefully to identify a ...
Kaixiong Zhou +4 more
doaj +1 more source
Robust Neural Architecture Search
CoRR, 2023 Neural Architectures Search (NAS) becomes more and more popular over these years. However, NAS-generated models tends to suffer greater vulnerability to various malicious attacks. Lots of robust NAS methods leverage adversarial training to enhance the robustness of NAS-generated models, however, they neglected the nature accuracy of NAS-generated ...
Xunyu Zhu +3 more
openaire +2 more sources
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 +9 more
core +1 more source