🚀 Онлайн семинар по ML/DS от "SBERLOGA"
⌚ Четверг 4 февраля, 19.00 по Москве
👨🔬 Доклад Сергей Иванов (Париж, Критео) : "Boost then Convolve: Gradient Boosting Meets Graph Neural Networks" . Cергей один из лидеров графового комьюнити в ОДС и автор лучшего канала в тг по граф-мл
@graphML - подписывайтесь.
Абстракт.
Graph neural networks (GNNs) are powerful models that have been successful in various graph representation learning tasks. Whereas gradient boosted decision trees (GBDT) often outperform other machine learning methods when faced with heterogeneous tabular data. But what approach should be used for graphs with tabular node features? Previous GNN models have mostly focused on networks with homogeneous sparse features and, as we show, are suboptimal in the heterogeneous setting. In this work, we propose a novel 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. Our model benefits from end-to-end optimization by allowing new trees to fit the gradient updates of GNN. With an extensive experimental comparison to the leading GBDT and GNN models, we demonstrate a significant increase in performance on a variety of graphs with tabular features.
Доклад основан на работе:
https://openreview.net/forum?id=ebS5NUfoMKLСсылка на зум будет доступна ближе к началу доклада через чат
@sberlogawithgraphs - присоединяйтесь.
Записи докладов на ютубе:
https://www.youtube.com/c/SBERLOGA - подписывайтесь.
