onasta

3 ms·

1.
Show HN: TabPFN-3.5, a Tabular Foundation Model for messy real-world tables(priorlabs.ai)
9 points by onasta 16d ago|1 comments
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by onasta 16d ago
I'm excited to announce the release of our new Tabular Foundation Model TabPFN-3.5! It improves a lot on standard tabular prediction problems (rank 1 by far [TabArena]( https://github.com/autogluon/tabarena ) and m
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Show HN: RelArena-α – open-source releases for Relational Learning(github.com)
4 points by onasta 1mo ago|0 comments
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TabPFN-3: scaling foundation models for tabular data to 1M rows(priorlabs.ai)
9 points by onasta 5mo ago|0 comments
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Show HN: TabPFN Scaling Mode – Tabular Foundation Model on millions of rows(priorlabs.ai)
5 points by onasta 10mo ago|0 comments
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Show HN: TabPFN-2.5 – SOTA foundation model for tabular data(priorlabs.ai)
73 points by onasta 11mo ago|13 comments
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by onasta 2y ago
There have been a ton of improvements! Much better performance overall, way larger data size limit (1K-->10K rows, 100-->500 features), regression support, native categorical data and missing values handling, much better support for u
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by onasta 2y ago
TabPFN is better on numerical data since v1 (see figure 6 in the CARTE paper). CARTE's main strength in on text features, which are now also supported for TabPFN v2 API version ( https://github.com/PriorLabs/tabpfn-
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Show HN: TabPFN v2 – A SOTA foundation model for small tabular data(nature.com)
153 points by onasta 2y ago|44 comments
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by onasta 4y ago
Super interesting! Do you know the kind of data that it's usually used for? And in the remaining 80% to 60%, do NNs acccount for a large portion of the best models? Bonus question: are the stats you're mentioning publically availa