TabNet uses sequential attention with sparsemax masks for instance-wise feature selection and reports state-of-the-art results on several tabular classification and regression benchmarks, plus a masked-reconstruction self-supervised pretraining method.
XGBoost: Scalable GPU Accelerated Learning
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abstract
We describe the multi-GPU gradient boosting algorithm implemented in the XGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast, scalable training on multi-GPU systems with all of the features of the XGBoost library. We employ data compression techniques to minimise the usage of scarce GPU memory while still allowing highly efficient implementation. Using our algorithm we show that it is possible to process 115 million training instances in under three minutes on a publicly available cloud computing instance. The algorithm is implemented using end-to-end GPU parallelism, with prediction, gradient calculation, feature quantisation, decision tree construction and evaluation phases all computed on device.
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cs.LG 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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TabNet: Attentive Interpretable Tabular Learning
TabNet uses sequential attention with sparsemax masks for instance-wise feature selection and reports state-of-the-art results on several tabular classification and regression benchmarks, plus a masked-reconstruction self-supervised pretraining method.