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Tabular datasets are the last "unconquered castle" for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent specialized neural architectures.
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Sgdr: Stochastic gradient descent with warm restarts
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BOHB: Robust and efficient hyperparameter optimization at scalae
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Deep reinforcement learning that matters
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 2020
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Autogluon-tabular: Robust and accurate automl for structured data
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Neural oblivious decision ensembles for deep learning on tabular data
S. Popov, S. Morozov, and A. Babenko · 2020
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Hyperparameter ensembles for robustness and uncertainty quantification
Florian Wenzel, Jasper Snoek, Dustin Tran, and Rodolphe Jenatton · 2020
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Tabnet: Attentive interpretable tabular learning
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Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl
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