[R] New paper on Tabular DL: "On Embeddings for Numerical Features in Tabular Deep Learning"

Hi! We introduce our new paper "On Embeddings for Numerical Features in Tabular Deep Learning".

Paper: [https://arxiv.org/abs/2203.05556](https://arxiv.org/abs/2203.05556)

Code: [https://github.com/Yura52/tabular-dl-num-embeddings](https://github.com/Yura52/tabular-dl-num-embeddings)

TL;DR: using embeddings for numerical features (i.e. using vector representations instead of scalar values) can lead to significant profit for tabular DL models.

Let's consider the vanilla MLP taking two numerical inputs.

https://preview.redd.it/yb55tdw27wn81.png?width=330&format=png&auto=webp&s=a6fc53e8611baee6993aab47480f0a6a6b85e46c

Now, here is the same MLP, but now with embeddings for numerical features:

https://preview.redd.it/zebl8tld7wn81.png?width=368&format=png&auto=webp&s=3d20652075d0543c7d6c70f34d67140bc2c6346b

The main contributions:

* we show that using vector representations instead of scalar representations …

deep learning dl features learning machinelearning numerical paper tabular