新闻
Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers systematically compared regression loss functions for tabular deep learning, finding quantile loss outperforms MSE, but identifying early stopping limitations.
- 为何重要
- Matters for engineers tuning neural networks on tabular data who want empirical guidance on objective functions and training procedures.
- 注意
- Single global early stopping checkpoint compromises performance across regions with different noise levels, even with heteroskedastic-aware objectives.
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