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1. Knowledge-aware evolutionary graph neural architecture search NSTL国家科技图书文献中心

Wang C. |  Zhao J.... -  《Knowledge-based systems》 - 2025,309(Jan.30) - 1.1~1.15 - 共15页

摘要:© 2024 Elsevier B.V.Graph neural architecture |  search (GNAS) can customize high-performance graph |  neural network architectures for specific graph tasks | , ignoring the prior knowledge that may improve the search |  the multi-objective evolutionary search on a new
关键词: Evolutionary transfer optimization |  Graph neural architecture search |  Graph neural network

2. Efficient Self-Supervised Neural Architecture Search NSTL国家科技图书文献中心

Sri Aditya Deevi |  Asish Kumar Mishra... -  《2025 19th International Conference on Ubiquitous Information Management and Communication》 -  International Conference on Ubiquitous Information Management and Communication - 2025, - 1~8 - 共8页

摘要: Neural Architecture Search (NAS) is a promising area to | Deep Neural Networks (DNNs) have successfully |  expert practitioners' careful design of neural |  memory to arrive at an optimal architecture. Automated |  lengthy search time, high processor needs, and a
关键词: Accuracy |  Memory management |  Manuals |  Inspection |  Neural architecture search |  Information management

3. Scalable reinforcement learning-based neural architecture search NSTL国家科技图书文献中心

Amber,Cassimon |  Siegfried,Mercelis... -  《Neural computing & applications》 - 2025,37(1) - 231~261 - 共31页

摘要: architecture search agent aimed at amortizing the initial | We assess the feasibility of a reusable neural |  time-investment in building a good search strategy |  best way to modify a given neural network |  architecture. This is achieved using a transformer-based
关键词: Neural architecture search |  AutoML |  Deep learning |  Reinforcement learning

4. Dependency-Aware Differentiable Neural Architecture Search NSTL国家科技图书文献中心

Buang Zhang |  Xinle Wu... -  《Computer Vision - ECCV 2024,Part LV》 -  European Conference on Computer Vision - 2025, - 219~236 - 共18页

摘要:Neural architecture search (NAS) reduces the |  neural network architectures, among which differential |  for the search efficiency. Despite achieving |  a high-performance architecture distribution | . Specifically, we model the architecture distribution in DARTS
关键词: Neural architecture search (NAS) |  Differentiable NAS |  Architecture distribution |  Dependency-aware modeling

5. Network-aware federated neural architecture search NSTL国家科技图书文献中心

Goektug Ocal |  Atay OEzgoevde -  《Future generations computer systems》 - 2025,162(Jan.) - 107475.1~107475.15 - 共15页

摘要:. Neural Architecture Search (NAS) has emerged to |  automate the search for the best-performing neural |  introduce Network-Aware Federated Neural Architecture |  Search (NAFNAS), an open-source federated neural | . However, the success of DL relies on optimal Deep Neural
关键词: Neural architecture search |  Federated learning |  Network pruning |  Client selection |  Client grouping |  Network emulation

6. Efficient Neural Architecture Search: Computational Cost Reduction Mechanisms in DeepGA NSTL国家科技图书文献中心

Jesus-Arnulfo Barrad... |  Carlos-Alberto Lopez...... -  《Advances in Computational Intelligence. MICAI 2023 International Workshops,Part II》 -  Mexican International Conference on Artificial Intelligence - 2025, - 125~134 - 共10页

摘要:Neural Architecture Search (NAS) aims to |  automate the design process of Deep Neural Networks (DNN |  architectures of Convolutional Neural Networks (CNNs) for |  performance of the resulting architecture. The previous | ) without requiring profound domain knowledge. The Deep
关键词: Neural architecture search |  Cost reduction |  Convolutional neural networks

7. MCGRAN: Multi-conditional Graph Generation for Neural Architecture Search NSTL国家科技图书文献中心

Sathish Purushothama... |  Julian Stier... -  《Machine Learning,Optimization,and Data Science,Part I》 -  International Conference on Machine Learning,Optimization,and Data Science - 2025, - 302~316 - 共15页

摘要: in Neural Architecture Search. MCGRAN learns neural | . With proper conditioning MCGRAN can generate neural |  the generated neural network architectures are novel |  and unique. Using an iterative conditional search | , we show that neural network architectures can
关键词: Graph generative models |  Neural architecture search

8. Contrastive meta-reinforcement learning for heterogeneous graph neural architecture search NSTL国家科技图书文献中心

Zixuan Xu |  Jia Wu -  《Expert Systems with Application》 - 2025,260(Jan.) - 125433.1~125433.13 - 共13页

摘要: Heterogeneous Graph Neural Architecture Search (CM-HGNAS). Our | Heterogeneous Graph Neural Networks (HGNNs |  effective HGNN architecture is a challenging task that | . Fortunately, the advent of Heterogeneous Graph Architecture |  Search (HGNAS) has automated this process and yielded
关键词: Heterogeneous graph neural architecture search |  Meta-learning |  Contrastive learning

9. Listwise ranking predictor for evolutionary neural architecture search NSTL国家科技图书文献中心

Li, Nan |  Ma, Lianbo... -  《Swarm and Evolutionary Computation》 - 2025,96 - 101956.1~101956.15 - 共15页

摘要:In evolutionary neural architecture search |  directly predict the ranking of each architecture instead |  architecture. Specifically, the training data is constructed |  (ENAS), the accuracy predictors (i.e., regression |  models) have been successfully applied to save
关键词: Neural architecture search |  Listwise ranking predictor |  Random encoding-combination

10. Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting NSTL国家科技图书文献中心

DONGHUI CHEN |  LING CHEN... -  《ACM transactions on knowledge discovery from data》 - 2025,19(1) - 11.1~11.23 - 共23页

摘要:-aware neural architecture search framework for MTS |  learning, and neural architecture search modules are |  any prior knowledge. For MTS forecasting, a search | Multivariate time series (MTS) forecasting has |  attracted much attention in many intelligent applications
关键词: Multivariate time series forecasting |  neural architecture search |  graph learning |  multi-scale decomposition
检索条件Neural architecture search

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