Attention Augmented Differentiable Forest for Tabular Data

Review of paper by Yingshi Chen, Xiamen University, 2020

The author has developed a new “differentiable forest”-type neural network framework for predictions on tabular data that has some similarity to the recently suggested NODE architecture and employs squeeze-and-excitation “tree attention blocks” (TABs) to show performance superior to gradient boosted decision trees (e.g. XGBoost, LightGBM, Catboost) on a number of benchmarks.


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