Today I’m speaking with Yury Gorishniy about the state of the competition between Deep Learning and Gradient Boosted Decision Trees when it comes to tabular datasets, and about a recent paper he published that seems to take a stab at improving the state of deep learning on tabular datasets.

We discuss whether or not there exists a gap between deep learning and gradient boosted decision trees, what the future of a gap might look like, and the extent to which the embedding of numerical features can give deep learning architectures a necessary boost in performance.

Two of his recent papers are useful in this discussion:

• On Embeddings for Numerical Features in Tabular Deep Learning - https://arxiv.org/abs/2203.05556
• Revisiting Deep Learning Models for Tabular Data - https://arxiv.org/abs/2106.11959

You can find Yury in the following places:

• GitHub - https://github.com/Yura52
• Twitter - https://twitter.com/YuraFiftyTwo

Enjoy!