NVIDIA released Kumo Tabular, a family of tabular foundation models in Small, Medium, and Large sizes spanning roughly 28M to 215M parameters that predict new rows in a single forward pass with no tra…
NVIDIA released Kumo Tabular, an open foundation model for tabular classification and regression, on Hugging Face under the OpenMDW-1.1 commercial license, claiming first place on the TabArena, Beyond…
A controlled study across several versions of two major tabular foundation model (TFM) families found that feature engineering gains are concentrated in earlier model generations and become negligible…
A developer's independent test found that foundation models top TabArena's tabular leaderboard, beating tuned XGBoost 92.6% of the time, but the advantage vanishes when tree-based models receive all t…
Google announced TabFM, a pre-trained foundation model for tabular data, now available in preview in BigQuery, enabling instant predictions via SQL functions AI.PREDICT and AI.EVALUATE. Developed by G…
TabBench-LLM, a benchmark introduced by the TabArena team, evaluates large language models as few-shot in-context tabular classifiers against Random Forest and TabPFN v2, using 19 synthetic tasks and …
Tabular foundation models (TFMs) such as TabPFN from PriorLabs and TabFM from Google are now outperforming gradient-boosted trees like XGBoost, CatBoost, and LightGBM on the TabArena benchmark, challe…
Google Research released TabFM on July 1, a foundation model for tabular data that achieves zero-shot classification and regression with no training or hyperparameter tuning, beating Optuna-tuned XGBo…
Google released TabFM on July 1, a zero-shot foundation model for tabular data that generates predictions without training on the user's dataset, using in-context learning. The model achieves state-of…