Since AlexNet showed the world the power of deep learning, the field of AI has rapidly switched to almost exclusively focus on deep learning. Some of the main justifications are that 1) neural networks are Universal Function Approximation (UFA, not UFO 🛸), 2) deep learning generally works the best, and 3) it is highly scalable through SGD and GPUs. However, when you look a bit further down from the surface, you see that 1) simple methods such as Decision Trees are also UFAs, 2) fancy tree-based methods such as Gradient-Boosted Trees (GBTs) actually work better than deep learning on tabular data, and 3) tabular data tend to be small, but GBTs can optionally be trained with GPUs and iterated over small data chunks for scalability to large datasets. At least for the tabular data case, deep learning is not all you need.
In this joint collaboration with Kilian Fatras and Tal Kachman at the Samsung SAIT AI Lab, we show that you can combine the magic of diffusion (and their deterministic sibling conditional-flow-
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