Deep Tabular Learning
Boost-GNN
Introduced by Ivanov et al. in Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksBoost-GNN is an architecture that trains GBDT and GNN jointly to get the best of both worlds: the GBDT model deals with heterogeneous features, while GNN accounts for the graph structure. The model benefits from end-to-end optimization by allowing new trees to fit the gradient updates of GNN.
Source: Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
Papers
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
