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Enabling Reliability-Driven Optimization Selection with ...
World Scientific Publishing
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World Scientific Publishing
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由 J Wu 著作2020被引用 3 次 — This paper proposes a Gate Graph Attention Neural Network (GGANN)-based compilation optimization option selection model. The data flow and function-call ...
Enabling Reliability-Driven Optimization Selection with Gate ...
Semantic Scholar
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The deep neural network based on GGANN is extended and a learning model that learns the heuristics method for program reliability is built that improves the ...
Enabling Reliability-Driven Optimization Selection with Gate ...
OUCI
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OUCI
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There is not much research on program reliability. This paper proposes a Gate Graph Attention Neural Network (GGANN)-based compilation optimization option ...
Enabling Reliability-Driven Optimization Selection with ...
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由 J Wu 著作2020被引用 3 次 — This paper proposes a Gate Graph Attention Neural. Network (GGANN)-based compilation optimization option selection model. The data °ow and function-call ...
(PDF) Compilation Optimization Pass Selection Using Gate ...
ResearchGate
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ResearchGate
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2024年10月22日 — Enabling Reliability-Driven Optimization Selection with Gate Graph Attention Neural Network. November 2020 · International Journal of ...
Finding effective optimization phase sequences
ACM Digital Library
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由 P Kulkarni 著作2003被引用 149 次 — In this paper, we describe support in VISTA, an interactive compilation system, for finding effective sequences of optimization phases.
Graph neural networks: A review of methods and applications
ScienceDirect.com
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由 J Zhou 著作2020被引用 6820 次 — The graph attention network (GAT) ... It uses the confidence-driven scheme to adaptively select the starting node and determine the node updating sequence.
Optimization of Computer Network Reliability Based on ...
Springer
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Springer
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2024年6月26日 — This paper will discuss the optimization methods of computer network reliability from many aspects.Choosing high-quality network equipment is ...
Heterogeneous network and graph attention auto-encoder ...
arXiv
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arXiv
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由 JX Liu 著作2024 — As an unsupervised learning model, Graph Attention. Autoencoder (GATE) can reconstruct the node attributes of data through the encoder and decoder. Fig. 2 shows ...
Correlational graph attention-based Long Short-Term ...
ScienceDirect.com
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ScienceDirect.com
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由 S Han 著作2021被引用 40 次 — We propose a correlational graph attention-based Long Short-Term Memory network (CGA-LSTM), a nested network that nests the correlational attention mechanism.