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Most tabular machine learning workflows start the same way: clean the data, engineer features, tune hyperparameters, and train a new model from scratch.
Google Research’s TabFM proposes a very different approach: treat the dataset itself as context and make predictions in a single forward pass, without task-specific training.
In this episode, the focus is on how zero-shot foundation models for tabular data actually work, why tables require a different architecture from text, and how TabFM compares with the models that came before it.
You’ll learn:
* Why is in-context learning harder for tabular data? Exploring how permutation invariance across rows and columns makes standard sequence modeling a poor fit for tables.
* How do TabPFN, TabICL, and TabFM solve the problem differently? Breaking down three architectural approaches to the same challenge and the key ideas that distinguish them.
* How do you run these models in practice? Using TabFM, TabPFN, and TabICL on real tabular datasets and looking at the kind of predictive performance you can expect from them.
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