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TabICL is a state-of-the-art tabular learner [Qu et al 2025]. The key is its very rich prior, that is baked in a pre-trained architecture -a table foundation model-, and leveraged by in-context-learning. Thanks to clever choices, it is fast and scalable, efficient even without a GPU.

Note

Foundation models, pretrained and readily usable for many downstream tasks, have changed the way we process text, images, and sound. Can we achieve similar breakthroughs for tables? Here I explain why with “CARTE”, we’ve made significant headway.