A study of bidirectional knowledge transfer between Random Forests and Deep Neural Networks using proposed distillation methods, evaluated on classification and regression tasks across six datasets.
Ke et al., ”LightGBM: A Highly Efficient Gradient Boosting Decision Tree,”Advances in Neural Information Processing Systems, vol
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Cross-Paradigm Knowledge Distillation: A Comprehensive Study of Bidirectional Transfer Between Random Forests and Deep Neural Networks for Big Data Applications
A study of bidirectional knowledge transfer between Random Forests and Deep Neural Networks using proposed distillation methods, evaluated on classification and regression tasks across six datasets.