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Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping

Paper recorded by Signals 4 on 2026-10-09 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-09 on arXiv · recorded by Signals 4 on 2026-10-12

Category: cs.AI · 人工智能 · first seen 2026-10-12

Abstract

While deep learning models for tabular data have recently received significant research attention, the choice of objective functions for regression tasks remains an understudied degree of freedom. Several lines of research offer alternatives to the standard Mean Squared Error (MSE), but differences in datasets, backbones, and evaluation protocols make their practical benefits difficult to compare.

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#343 most recent of 960 cs.AI papers we have recorded · ↑ newer: Hide and Seek: HIDE Concepts to SEEK explanations for black box models · ↓ older: Task Context Shapes Component Roles in Language Models
Cite this page: Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping: the #343 most recent of 960 cs.AI papers we have recorded (as of 2026-10-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/revisiting-tabular-deep-regression-objectives-and-the-limits-of-global-early-sto.html
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