Researchers introduced Balanced Adaptive Prototype Selection (BAPS), a framework that compresses contexts for TabPFN inference on large-scale tabular data without retraining. Experiments on the millio…
Prior Labs' TabPFN, a pre-trained Transformer for tabular data, enables zero-shot machine learning predictions without traditional training, feature engineering, or hyperparameter tuning, according to…
Prior Labs' TabPFN, a pre-trained Transformer for tabular data, enables zero-shot machine learning predictions without manual feature engineering or hyperparameter tuning. The model integrates with Sc…
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…
SAP has closed its acquisition of Prior Labs, a Freiburg-based AI lab specializing in foundation models for structured tabular data, for more than €1 billion over four years. The deal, which keeps Pri…
Google Research introduced TabFM, a foundation model that treats tabular data predictions as an in-context learning problem, enabling predictions via a simple API call without traditional model traini…
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…
Google Research introduced TabFM, a foundation model for tabular data that performs zero-shot classification and regression without dataset-specific training. The model uses a hybrid-attention archite…
Google Research introduced TabFM, a zero-shot foundation model for tabular data classification and regression, integrated into BigQuery ML. The model uses in-context learning to eliminate manual train…