We've released the first version of our tabpfn #rstats package to CRAN. This is an interface to the Python #TabPFN package.
tidyverse.org/blog/2026/03...
The model is a pre-trained deep learning model that has performed exceedingly well on every data set I've tested it on.
New #J2C Certification:
Probabilistic Pretraining for Improved Neural Regression
Boris N. Oreshkin, Shiv Kumar Tavker, Dmitry Efimov
https://openreview.net/forum?id=F6BTATGXaf
#datasets #tabpfn #regression
Exploring TabPFN: A Foundation Model Built For Tabular Data The Tabular Foundation Model Moment For years, tabular data was the one domain where deep.... @cosmicmeta.ai #TabPFN
https://u2m.io/rREUaYdG
I just gave a 20m overview of the #TabPFN deep learning model at R/Pharma.
The slides are at: topepo.github.io/2025-r-pharma/
(link to Quarto files on the second slide)
Just presented our @fau.de @taltstidl.bsky.social #ML study at #TCT2025 🌉 demonstrating
👉 accurate & daily refined predictions of neurological outcome after #cardiacarrest based on routinely collected clinical & laboratory parameters is feasible with #TabPFN
🔗 www.tctmd.com/slide/tct-91...
Uncertainty‑Guided Model Selection Improves Biomolecule Predictions
Using TabPFN's inter‑quantile range to pick low‑uncertainty models improves siRNA knockdown predictions, beating simple averaging. The pre‑print was posted on 2 Oct 2025. getnews.me/uncertainty-guided-model... #tabpfn #siRNA
Kriging Prior Regression Boosts Soil Mapping Accuracy with TabPFN
Kriging prior Regression (KpR) paired with TabPFN boosted soil mapping accuracy, raising R² by about 30% across six LimeSoDa field datasets of organic carbon, clay and pH. getnews.me/kriging-prior-regression... #soilmapping #tabpfn #kriging
TabPFN Beats VBLL for Uncertainty Calibration in Medical Data
Research on three medical tabular datasets shows TabPFN outperforms a VBLL‑enhanced version in uncertainty calibration; the paper was submitted on 12 Sep 2025. getnews.me/tabpfn-beats-vbll-for-un... #tabpfn #vbll #uncertaintycalibration
It’s an impractical drain of time and money, especially when proven and efficient models like CatBoost can deliver faster, cheaper, and more reliable results.
#tabulardata #tabpfn #machinelearning #datascience
Let's build a reliable picture of TabPFN's strengths and weaknesses—grounded in evidence, not hype.
github.com/valeman/T...
#tabulardata #tabpfn
Concepts for working with little specific data to a given task have first gained significant relevance for AI with the first #GPT models. Their relevance has ever grown since. Thanks to architectures like #TabPFN it is also possible, to apply #FewShotLearning to applications, where precision is key.
Just listened to this fantastic episode about #TabPFN - using #DeepLearning for tabular data, including time-series data.
www.superdatascience.com/podcast/863
Based on the @nature.com paper: www.nature.com/articles/s41...
#TabPFN es un modelo fundacional para datos tabulares que supera a modelos tradicionales (así es, supera a modelos como RandomForest y GradientBoosting) en precisión, pero lo más increíble de todo es que NO REQUIERE ENTRENAMIENTO
arxiv.org/abs/2501.02945
#TabPFN for the win in time series @toates.bsky.social