Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Алгоритми за причинно-следствено откриване (PC, FCI, LiNGAM)× | Графова невронна мрежа с внимание (GAT)× | |
|---|---|---|
| Област≠ | Причинно-следствено заключение | Дълбоко обучение |
| Семейство≠ | Regression model | Machine learning |
| Година на възникване≠ | 2000 | 2018 |
| Създател≠ | Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM) | Veličković, P. et al. |
| Тип≠ | Causal structure learning | Graph neural network (attention-based) |
| Основополагащ източник≠ | Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402 | Veličković, P. et al. (2018). Graph Attention Networks. ICLR. link ↗ |
| Други названия≠ | PC algorithm, FCI algorithm, LiNGAM, causal structure learning | Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network |
| Свързани≠ | 5 | 4 |
| Резюме≠ | Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges. | 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). |
| ScholarGateНабор от данни ↗ |
|
|