A mixture-of-experts model with test-time self-supervised weight adjustment improves tabular imbalanced regression under three different test distributions, reporting a 7.1% average MAE gain.
In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28
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Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalanced Regression
A mixture-of-experts model with test-time self-supervised weight adjustment improves tabular imbalanced regression under three different test distributions, reporting a 7.1% average MAE gain.