Google has added TabFM, a pre-trained foundation model for tabular data, to BigQuery in preview, enabling enterprise teams to generate classification and regression predictions from structured data wi…
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…
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…
A developer released Zer0Fit, an open-source project that wraps Google's TabFM and TimesFM foundation models as a Model Context Protocol server, enabling LLM agents to invoke zero-shot ML tasks with n…
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…