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[QA] Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Data
from Arxiv Papers Hosted by Igor Melnyk
Real-TabPFN enhances tabular data performance by continued pre-training on curated real-world datasets, outperforming models trained on broader datasets, achieving significant gains on 29 OpenML AutoML Benchmark datasets. https://arxiv.org/abs//2507.03971 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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This episode was published on July 8, 2025.
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