arXiv 2609.37959 · cs.LG · 2026-09-29
TabFM: A Zero-Shot Foundation Model for Tabular Data
Weihao Kong, Erez Louidor Ilan, Shuxin Nie, Taman Narayan, Rajat Sen, Yichen Zhou, Deqing Fu, Samet Oymak 等 9 人
在最新榜 #38 本站收录于 2026-10-21
一句话要点
TabFM 是 4 亿参数的表格基础模型,全靠合成数据训练,单次前向即可零样本预测。
TabFM is a 400M-parameter tabular foundation model trained on synthetic tables that predicts zero-shot in a single forward pass.
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主题与学科
同主题:大语言模型
- Jeva: System One Models from First Principles ▲371
- Post-Training Leaves Behavioral Shadows on Unrelated Decisions ▲273
- OmniEdu: Open Foundation Models for Learning and Teaching ▲240
- PAR: Progress-Aligned Recurrence for Length Generalization in State Tracking ▲148
- MemT-1.0: Towards Memory-Native Evolution ▲119
数据说明
元数据来自 arXiv API(已核实);点赞来自 Hugging Face 每日论文;引用来自 Semantic Scholar。最近刷新 2026-10-22。
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