Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Grafová pozornostní síť× | Rekurentní neuronová síť× | |
|---|---|---|
| Obor | Hluboké učení | Hluboké učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2018 | 1986–1990 |
| Tvůrce≠ | Veličković, P. et al. | Rumelhart, D. E.; Elman, J. L. |
| Typ≠ | Graph neural network (attention-based) | Sequential neural network |
| Původní zdroj≠ | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| Další názvy | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network | RNN, Elman network, Jordan network, simple recurrent network |
| Příbuzné≠ | 4 | 3 |
| Shrnutí≠ | The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN). | A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models. |
| ScholarGateDatová sada ↗ |
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