**SAINT** is a hybrid deep learning approach to solving tabular data problems. SAINT performs attention over both rows and columns, and it includes an enhanced embedding method. The architecture, pre-training and training pipeline are as follows: 

- $L$ layers with 2 attention blocks each, one self-attention block, and a novel intersample attention blocks that computes attention across samples are used.
- For pre-training, this involves minimizing the contrastive and denoising losses between a given data point and its views generated by [CutMix](https://paperswithcode.com/method/cutmix) and [mixup](https://paperswithcode.com/method/mixup). During finetuning/regular training, data passes through an embedding layer and then the SAINT model. Lastly, the contextual embeddings from SAINT are used to pass only the embedding corresponding to the CLS token through an [MLP](https://paperswithcode.com/method/feedforward-network) to obtain the final prediction.

The **Exponential Linear Squashing Activation Function**, or **ELiSH**, is an activation function used for neural networks. It shares common properties with [Swish](https://paperswithcode.com/method/swish), being made up of an [ELU](https://paperswithcode.com/method/elu) and a [Sigmoid](https://paperswithcode.com/method/sigmoid-activation):

$$f\left(x\right) = \frac{x}{1+e^{-x}} \text{ if } x \geq 0 $$
$$f\left(x\right) = \frac{e^{x} - 1}{1+e^{-x}} \text{ if } x < 0 $$

The Sigmoid part of **ELiSH** improves information flow, while the linear parts solve issues of vanishing gradients.

ELiSH

The Quest for the Golden Activation Function

SAINT

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

**IB-BERT**, or **Inverted Bottleneck BERT**, is a [BERT](https://paperswithcode.com/method/bert) variant that uses an [inverted bottleneck](https://paperswithcode.com/method/inverted-residual-block) structure. It is used as a teacher network to train the [MobileBERT](https://paperswithcode.com/method/mobilebert) models.

Source	SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com